an anticipative crowd management system preventing clogging in exits during pedestrian evacuation...

IEEE SYSTEMS JOURNAL, VOL. 5, NO. 1, MARCH 2011 129

An Anticipative Crowd Management SystemPreventing Clogging in Exits During

Pedestrian Evacuation ProcessesIoakeim G. Georgoudas, Georgios Ch. Sirakoulis, Member, IEEE, and Ioannis Th. Andreadis

Abstract—This paper presents an anticipative system whichoperates during pedestrian evacuation processes and preventsescape points from congestion. The processing framework ofthe system includes four discrete stages: a) the detection andtracking of pedestrians, b) the estimation of possible route forthe very near future, indicating possible congestion in exits, c)the proposal of free and nearby escape alternatives, and d) theactivation of guiding signals, sound and optical. Detection andtracking of pedestrians is based on an enhanced implementationof a system proposed by Viola, Jones, and Snow that incorporatesboth appearance and motion information in near real-time. Atany moment, detected pedestrians can instantly be defined asthe initial condition of the second stage of the system, i.e., theroute estimation model. Route estimation is enabled by a dynamicmodel inspired by electrostatic-induced potential fields. Themodel combines electrostatic-induced potential fields to incor-porate flexibility in the movement of pedestrians. It is based onCellular Automata (CA), thus taking advantage of their inherentability to represent effectively phenomena of arbitrary complexity.Presumable congestion during crowd egress, leads to the promptactivation of sound and optical signals that guide pedestrianstowards alternative escaping points. Anticipative crowd manage-ment has not been thoroughly employed and this system aims atconstituting an effective proposal.

Index Terms—Cellular automata, congestion, crowd modeling,pedestrian evacuation, potential fields.

I. INTRODUCTION

S AFETY in public facilities is a dominant concern, con-tinuously active, not affording complacency. Thus, it has

become an interdisciplinary topic of interest, a point of inter-section among different scientific areas, an issue subject to in-creasing thorough research. Nowadays, crowd dynamics andactive visual surveillance of moving objects are drawing ex-tended scientific interest in order to improve crowd safety inpublic facilities. Deep insight in crowd dynamics has resultedin better understanding of pedestrian behavior as well in sub-stantial changes regarding the architecture of such construc-tions. Crowd safety and comfort in congested places not only

Manuscript received March 18, 2010; revised July 18, 2010 and September12, 2010; accepted September 27, 2010. Date of publication December 06, 2010;date of current version February 18, 2011.

The authors are with the Department of Electrical and Computer Engineering,Laboratory of Electronics, Democritus University of Thrace, GR-671 00 Xanthi,Greece (e-mail: [email protected]; [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSYST.2010.2090400

depend on the design and the function of the area, but also onthe behavior of each individual. Investigation of crowd underpanic has indicated that individuals in such situations developherding behavior and clogging, thus vitiating the effective use ofall means of emergent evacuation [1]. Abrupt behaviors of thecrowd, rather than the actual cause of the disaster, bring mostinjuries or losses [2]. These behaviors refer to the destructiveactions that a crowd may experience during a disaster, such asstampede, pushing others out of the way, knocking others downor trampling on others.

A multitude of approaches simulating pedestrian dynamicshas been reported in the literature; CA-based [3], lattice-gasand social force models [1], fluid-dynamic [4] and agent-based[5], methods related to game theory [6], and experiments withanimals [7]. All approaches can be qualitatively distinguished,focusing on different characteristics that each of them domi-nantly display. In some models, pedestrians are ideally consid-ered as homogeneous individuals, whereas in others, they aretreated as heterogeneous groups with different features (e.g.,gender, age, psychology). There are methods, where collectivephenomena emerge from the complex interactions among indi-viduals (self-organizing effects), thus describing pedestrian dy-namics in a microscopic scale. Other methods treat crowd as awhole, modeling pedestrian dynamics on a macroscopic scale.There are models discrete in space and time and others spa-tial-temporally continuous.

Recent approaches suggest that crowd consists of discrete in-dividuals able to react with their surroundings. That requiresa large number of computations. A possible solution could bemodern computer power combined with the use of a CA com-putational model. CA are very effective in simulating physicalsystems and solving scientific problems, since they can capturethe essential features of systems where global behavior arisesfrom the collective effect of simple components that interactlocally [8]. Furthermore, the evacuation process is inherentlycomplex, i.e., a system multi-parameterised and its responsecannot be easily estimated. There are interactions among pedes-trians, buildings and environment and there are also socio-psy-chological parameters which should be taken under considera-tion [9]. Evacuation could be defined as a non linear problemwith many factors affecting it. A system of Partial DifferentialEquations (PDEs) could effectively approach it. The result is asystem of PDEs very difficult to handle, which would also bedemanding in terms of computer power and computation time.CA can act as an alternative to PDEs [10]. Literature reports avariety of CA-based models investigating crowd behavior under

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130 IEEE SYSTEMS JOURNAL, VOL. 5, NO. 1, MARCH 2011

different circumstances. The impact of environmental condi-tions [11] and bidirectional pedestrian behavior [12] have beenstudied. Interactions among pedestrians, friction effects [13] andherding behavior [14] have also been considered. Furthermore,CA originated models focus on human behaviors, such as iner-tial effects, unadventurous effect and group effect [15] or treatpedestrians as particles subject to long-range forces [16].

Parallel to deep insight in crowd dynamics, significant rolein crowd safety management plays visual surveillance. Towardsa more effective management of crowd in congestion, explicitstudy of visual surveillance in human-involving dynamic sceneshas led to a wide spectrum of promising applications [17]. Amajor problem in active surveillance is occlusion handling. Typ-ically, during occlusion, only portions of each object are vis-ible and often at very low resolution. Traditional closed-circuittelevision (cctv) systems are proven ineffective as the numberof cameras exceeds the capability of human operators to mon-itor them. Visual surveillance in dynamic scenes attempts to de-tect, recognize and track certain objects from image sequences,or even to understand and describe object behaviors. Imple-mentation of real-time, display and image processing systemsare rather difficult due to the huge amount of data that is pro-cessed. Nevertheless, models for multiple people tracking basedon video technology and sensor networks have been developedefficiently [18]. In general, the processing framework of vi-sual surveillance in dynamic scenes includes a set of successivestages; environmental modeling, detection of motion, classifica-tion of moving objects, tracking and human identification frommultiple cameras [17]. Particularly, the aim of motion detectionis segmentation of moving objects from the rest of an image.A common method is the application of background subtrac-tion algorithms [19]. The method is simple but extremely sensi-tive to changes in dynamic scenes. Optical-flow based methoduses characteristics of flow vectors of moving objects over timeto detect moving regions in an image section [20]. The majordrawback of the method is that it is computationally complexand very sensitive to noise. Hence, it can hardly be applied tovideo streams in real-time.

Another conventional approach, temporal differencing,makes use of the pixel differences between two successiveframes in a sequence of images to extract moving regions. Themethod is very adaptive to dynamic environments [21]. Varioustracking methods are reported in literature. There are algorithmsthat apply tracking based on variations around image regionsthat correspond to moving objects [22]. Another approach thatreduces computational complexity performs tracking by dy-namically updating bounding contours which correspond to theoutlines of the moving elements [23]. Furthermore, matchingbased tracking is established either by extracting elements withcertain features and then matching features between images[24], or by matching image elements to models of a data basis[25].

Regarding occlusion, the problem can hardly be addressedsince motion segmentation may become unreliable. When mul-tiple moving objects occlude each other, especially when theirspeeds, directions and shapes are very close, their motion re-gions coalesce, which makes the location and tracking of objectsparticularly difficult. Manipulating the problem by using statis-

tical methods into available image information downgrades theresponse of the system [17].

It is recommended [26] a combination of measures, with re-gard to crowd management. The infrastructure should be de-signed in a way that no bottlenecks or objects will obstruct theflow and accumulations of large crowds, counter-flows or in-tersecting flows should be avoided. Furthermore, areas of accu-mulation must be monitored and contingency plans should beworked out and exercised in advance. As mentioned though, itwould be even more favorable in crowd management, a simula-tion tool for the prediction of the flows that would allow an an-ticipative crowd management. Such a perspective has not beenthoroughly employed.

In this paper, it is proposed an integrated system that op-erates as an anticipative crowd management tool in cases ofmedium density crowd evacuation. Preliminary real data eval-uation processes indicate that it responds fast in order to pre-vent clogging in exits under emergency conditions. The systemconsists of three modules; the detecting and tracking algorithm[21], the model of possible route estimation and the sound andoptical signals. The initialization process is originated from thedetecting and tracking algorithm, which is supported by cam-eras. The automatic response of the algorithm provides the lo-cation of pedestrians around escape points at any time, thus pro-viding instant initialization data to the model of possible routeestimation. However, its role is not confined exclusively for ini-tialization purposes. Instead, it also operates as a control andrectifying mechanism, by checking and correcting the responseof the CA model periodically. The response of the route esti-mation model is compared to the output of the tracking algo-rithm. In cases of large differences, the model is re-initializedaccording to the current conditions of the tracking algorithm.Finally, sound and optical signals enable the system to redirectpedestrians, enhancing its effectiveness and efficiency (Fig. 1).

System operation is developed in four successive stages, set-ting out with the detection and tracking of pedestrians that en-able dynamic initialization and continuing with the estimationof their possible route for the very near future. Then, amongall possible exit points, the most suitable is proposed as an al-ternative, triggering the activation of appropriate guiding sig-nals, sound and optical. The criterion of suitability is the dis-tance of the congested exit from an alternative one. Hence, theclosest free exit is preferred. Detection and tracking of pedes-trians are based on an enhanced implementation of the algo-rithm proposed in [21] that incorporates both appearance andmotion information in near real-time. Detection is succeeded bysetting thresholds to a linear combination of filters. A dynamicmodel originated from electrostatic-induced potential fields en-ables route estimation. Based on CA, the model takes advantageof their inherent ability to represent sufficiently phenomena ofarbitrary complexity. It combines electrostatic-induced poten-tial fields in order to incorporate smoother pedestrian move-ments. Congestion in exits during crowd egress, leads to theprompt activation of sound and optical signals that guide pedes-trians towards alternative escaping points.

The following sections describe thoroughly the structuralparts of the system. Particularly, in Section II, the fundamentalarchitectural concepts of the CA-based evacuation model are

GEORGOUDAS et al.: ANTICIPATIVE CROWD MANAGEMENT SYSTEM 131

Fig. 1. Schematic diagram of a system for dynamic guidance of crowd evacuation under emergency conditions.

Fig. 2. Distinct features of evacuation process.

explained, whereas Section III presents the principles of thedetection and tracking algorithm and its incorporation in thesystem. In Section IV, elaborations on methodology are pre-sented, whereas in Section V examples as well as empirical andsimulation results further clarify the operation of the systemand evaluate its efficiency. Finally, the conclusions drawn alongwith directions of future work are discussed in Section VI.

II. DESCRIPTION OF THE CA-BASED EVACUATION MODEL

Evacuation modeling is CA originated, thus allowing the em-ployment of powerful and sophisticated computation techniquesfor the development of useful modeling tools. Models based onCA lead to algorithms, which are fast because they exploit theinherent parallelism of the CA structure [27]. The model is ma-trix-based and discretises a floor area into CA cells. Each cellmay represent a free floor area, an obstacle, an area occupiedby a pedestrian, or a region with other attributes. Its size is as-sumed as equal to the minimum area that a person could oc-cupy in medium to high density conditions, i.e., 0.4 m 0.4m [3]. Pedestrian movement is defined in a local state, by rules

that relate the state of a cell to that of each closest neighbor-hood. Consequently, the model is inherently emergent and in-teractions among simple parts lead to the emulation of complexphenomena, such as crowd dynamics.

Macroscopical, distinct attributes of crowd evacuation [1] areessential to the model. Particularly, clogging in front of exits,crowd transition to incoordination due to clogging, peoplequeuing or developing herding behavior, i.e., following thebehavior of other people, extensively appear during simulations(Fig. 2). CA-driven computer simulations proved that organ-isms that show flocking behavior do not need to communicateglobally in order to coordinate their actions, but it is adequatethat they are able to respond only with their near neighbors [28].Moreover, consistent research efforts proved that in a systemof self-propelled particles as the amount of noise decreases aphase transition from a disordered to an ordered state takesplace [29].

In some cases, the computational model simplifies the behav-ioral representation of individuals, by employing one decisionrule (based on the assumption of the least effort) to represent thecomplex nature of individual behaviors. Furthermore, individ-uals are considered similar in terms of decision-making process,although they can be assigned different characteristics regarding


Fig. 3. Two successive snapshots of pedestrian simulation. Pedestrians markedwith X, move considerably slower than all other pedestrians, indicating a dif-ferent group of people.

velocity. For instance, in Fig. 3, pedestrians marked with X,move considerably slower than all others, indicating a differentgroup of people. Nevertheless, most simulations have been car-ried out adopting that all pedestrians are the same in terms ofsize, sex, age and mobility. In [30], it is argued that the pedes-trian velocities (in an undisturbed situation) can be consideredas a normal distribution and an estimate of the average velocitywould be 1.5 . Acceleration and braking times have beenconsidered negligible, whereas the grid has been assumed ho-mogeneous and isotropic. Consequently, a model of maximumvelocity equal to unity is formed, meaning that movements to-wards the closest neighbors are only performed.

The model has been further tested by comparing several cor-responding routes of individuals or group of people. All routesare derived from the detection and tracking algorithm as well asthe route estimation CA model. Fig. 4 displays three differentroutes comparisons; two for individuals and one for a group ofpeople as formed from both means, i.e., the tracking algorithmand the CA model. In Fig. 4(a), the tracked routes of two singlepersons are displayed as they move towards the exit points.Fig. 4(b) shows the corresponding routes simulated by the CAmodel. In Figs. 4(c) and 4(d) the route comparison of a group ofpeople is depicted. In the latter case, different colors correspondto the location of the group at different times. Tracked and sim-ulated results share acceptable approximation.

The theoretical background of the model stems from elec-trostatic-induced potential fields, which provide the essential

Fig. 4. (a) Route of two persons as derived from the tracking algorithm and(b) the corresponding routes as extracted from the CA-based model. Red bulbsindicate the location of exit points. (c) The routes of a group of four peopleas derived from the tracking algorithm and (d) the corresponding routes as ex-tracted from the CA-based model. Different colors correspond to the locationof the group at different times.

knowledge of the whole route. The distance calculation arisesfrom a potential field based on the Euclidean metric. Further-more, it is adopted an efficient method to overcome trouble-in-ducing obstacles by shifting the moving mechanism to a poten-tial field method based on Manhattan distance. This metric is


defined as the sum of the absolute values of the differences inx- and y-coordinate [31].

The electrostatic fields derive from negative charges, whichare placed at exit points and generate attractive forces uponpedestrians as well as from positive charges that are placedwhere obstacles and walls exist and generate repelling forcesupon them. Each pedestrian is represented by a test charge,of such a small magnitude that has a negligible affect on thefield around the point it is placed. The field is described by theelectrostatic Coulomb force [32]:

(1)In (1), corresponds to the resultant force upon a charge,and represent the magnitude and the position of the elec-tric source respectively, is the unit vector in the direction of

, i.e., a vector pointing from charge (electricsource) to charge and is the magnitude of , i.e., the dis-tance between charges and , whereas is the correspondingelectric field. Finally, represents the total number of electricsources. The unit vector in space is expressed, in Cartesian no-tation, as a linear combination of and andthe values of its scalar components are equal to the cosine of theangle formed by the unit vector with the respective basis vector.

The approach is relaxed in some extent, in order to be ap-plicable to pedestrian motion. More specifically, each boundedarea that includes an exit, i.e., a regional field, corresponds to anindependent level. The adoption of the latter term implies thateach room with its exits corresponds to a separate electric field,independent from all other fields around. Thus, coupling effectsamong different electrical fields are avoided and an individualis able to leave a room and move towards another one, withoutbeing affected by the exit already left. The resultant force upona test charge converges to the closest attractive charge, whichcorresponds to the closest exit, thus generating the direction ofmovement for each pedestrian.

Furthermore, the model adopts a computationally fast andefficient method to overcome cases of obstacles that may trappedestrians to an endless recurrent route or force them to stayimmovable, e.g., U-shaped obstacles. The motion rule shifts to amechanism less demanding regarding computational resources,described in a CA driven model that uses a potential field ap-proach based on Manhattan distance. The gradient descent onthe potential function designates the direction of movement,thus introducing a kind of global space knowledge for all pedes-trians.

The potential field is generated from the exit points. Theshortest exit route is found by choosing the direction of apedestrian cell that has the lowest value on the potential field.Now, the field is scalar and the method becomes advantageousin terms of computational speed, because the only quantityinvolved is the value of the potential field. On the other hand,the calculated direction of movement is less precise comparedto the electrostatic force field approach, which is based on theEuclidean distance. The potential field generation involves eachcell flooding its incremented count to all adjacent cells andto all diagonal cells. If a new value is flooded to another cell,

the previous value is compared and the lowest is stored. Thisrecursive flooding provides an approximation to the shortestEuclidean distance, i.e., the Manhattan distance, between theexit and any other point around. This method has been usedin robot path planning applications [33]. From a mathematicalpoint of view, the entrapment of a pedestrian can be explainedby assuming that the potential has a local minimum at thispoint, under the constraints of the obstacle.

This problem has been encountered by limiting the numberof iterations around the entrapment point. As soon as the limitis reached, the pedestrian chooses a different direction, thus, re-leased from the entrapment point. If a cell is occupied by an ob-stacle, then it will not flood its neighbors. This is important sinceit allows refraction to occur around obstacles, permitting an in-dividual to overcome them. Furthermore, the whole process isgenerated in a local state. It is developed in a CA simulation plat-form, where only local interactions take place, in parallel amongcells. Fig. 5 displays the effective response of the model undercomplicated conditions and illustrates the evolution of evacua-tion as well. As displayed, pedestrians are directed through in-terior exits towards the general one, overcoming U-shaped ob-stacles by moving around their borders.

Algorithmically, the realization of the aforementionedmethod is based on an matrix, calculated for each occu-pied cell. The elements represent all possible updated spatialand temporal states of the occupied cell. Variable indicates thenumber of the exits. Each element of the th rowspecifies the Manhattan distance of the occupied cell and itseight neighbors from the th exit. The occupied cell is alwaysrepresented by the fifth cell of each row, whereas all other cellsrepresent the eight closest neighbors (Moore neighborhood).As soon as all possible routes are detected, i.e., as soon as eachof the elements of the matrix is calculated, the shortestprevails. Consequently, both the destination exit and the direc-tion during the following step are defined simultaneously, asrepresented by the row and the column of the minimum valueelement respectively.

III. THE DETECTION AND TRACKING ALGORITHM

The detection and tracking algorithm originates from thepedestrian detection system proposed by Viola, Jones, andSnow [21] that incorporates both appearance and motioninformation in near real-time. Its reimplementation in [34]illuminates the merits and the drawbacks of their approach.The system has been further enriched with attributes at variousstages, enhancing its applicability. Solutions that facilitatevideo file decomposition into frames and their input process tothe system have been successfully incorporated in the system.

The method is based on a set of simple sum-of-pixel filtersthat are boosted into a robust pedestrian classifier. Boostingrefers to the general problem of producing an accurate predic-tion rule by combining rough and moderately inaccurate weakhypotheses. Computationally, it is employed as a machinelearning algorithm for performing supervised learning. Thelatter is a technique for deducing a function from training datathat consist of pairs of input objects and desired outputs. Theoutput of the function can predict a class label of the inputobject (classification).


Fig. 5. (a)–(c). Successive screenshots displaying the evolution of the evacua-tion and the overcome of U-shaped obstacles.

Formally, the booster is provided with a set of labeled trainingexamples , where is the label associ-ated with instance : for example, might be the observabledata associated with a race and the outcome of that race. Oneach round , the booster devises a distributionover the set of examples, and requests a weak hypothesis withlow error in respect to . Thus, distribution specifies therelative importance of each example for the current round. After

rounds, the booster must combine the weak hypotheses intoa single prediction rule [35].

Fig. 6. Input image is passed through a sequence of classifiers. An image isclassified as a pedestrian if it passes through all classifiers. Classification stopsif any of the stages fail.

AdaBoost, short for Adaptive Boosting, is the algorithmadopted for the training of the detector. Whereas boosting is notalgorithmically constrained, AdaBoost consists of iterativelylearning weak classifiers and adding them to a final strongclassifier (cascade architecture, Fig. 6). A weak learner isdefined as a classifier, which is only slightly correlated withthe true classification (it can label examples better than randomguessing). In contrast, a strong learner is a classifier arbitrarilywell correlated with the true classification. AdaBoost calls aweak classifier repeatedly in a series of rounds, .For each call, a distribution of weights is updated and indicatesthe importance of examples in the data set for the classifica-tion. On each round, the weights of each incorrectly classifiedexample are increased (or alternatively, the weights of eachcorrectly classified example are decreased), so that the newclassifier focuses more on those examples. Hence, the algorithmconstructs a strong classifier as a linear combination of simple,weak classifiers.

The system successively applies simple features to windowsin each frame. Detection is then achieved by defining a thresholdto a linear combination of these simple filters. Each featureis a simple sum-of-pixels filter with an associated weight andthreshold. For performance reasons, the features are applied se-quentially in a cascade so that only regions of an image likelyto contain a pedestrian are examined by later features. The sim-plicity of the filters enables the system to run fast. Motion infor-mation is incorporated by taking differences between successiveframes. Shifted versions of these difference images capture localtranslational motion information. The major benefit of the tech-nique followed is that it requires much less computation thanothers for motion capture. For instance, optical flow possiblyprovides more motion information than difference images, butit is prohibitively expensive to compute [21].

The dynamic pedestrian detector is based on the simple rec-tangle filters [36]. They measure the differences between re-gion averages at various scales, orientations, and aspect ratios.Sum-of-pixel filters are composed of a set of oriented rectanglesand their response is the sum of the intensities of the pixels in therectangles. They can distinguish pedestrians from non-pedes-trians and their responses are fast enough to be computed. Theresponse of a filter is described by the following equation:

(2)

where represents the orientation of each rectangleand , the response of each rectangle that com-pose the filter. In particular, assuming that is an image and


are the upper left and lower right corners re-spectively, of a rectangle within the image, then the response ofthe rectangle is

(3)

Information about the direction of motion can be extractedfrom the difference between an image at time and shifted ver-sions of the successive image at time . Five kinds of differ-ence images are used in the detection system:

(4)

where is the image at time andsubscripts imply operations that shift an image one pixel up,down, left, and right respectively.

Scale invariance is achieved during the training process.A hierarchy of difference images at various scales is pro-duced. Scaled difference images are arranged in levels in apyramid structure. Each level of the pyramid representationof is constructed by first scaling the originalinput image by a scale factor and then constructing thedifference images for that level following (5):

(5)

where is image scaled by factor . Scaling stopswhen is less than 20 15 pixels.

The algorithm has been further enriched, introducing elab-orating video file decomposition mechanisms and furtherstrengthening of filtering processes that improve its efficiencyand broaden its applicability. All these features are thoroughlydescribed in the following section.

In practice, self-occlusion and occlusions between differentmoving objects or between moving objects and the backgroundare inevitable. When occlusion is slight, a single camera isgenerally sufficient to detect and track objects. However, whenthe density of objects is high, the resulting occlusion andlack of visibility suggests the settlement and use of multiplecameras. Their collaboration contributes to the detection ofan object using information available from all the camerasin the scene. Tracking with a single camera easily generatesambiguity due to occlusion or depth. This ambiguity may beeliminated from another view. In this case, the aforementionedmultiple cameras system supporting the tracking algorithmcould be proven extremely helpful because the surveillancearea would be expanded and multiple view information could

overcome occlusion. More cameras could offer an efficientmethod for coping with occlusion by choosing the “best” view[37], resulting however in increased overall system cost.

IV. ELABORATIONS ON THE METHODOLOGY

Detection and tracking algorithm that realizes the initializa-tion process of the system requires four preliminary steps toenable its training. The human factor in this process is neces-sary. The very first one defines the decomposition of a recordedvideo into its frames in order the training process to commence.A script written in Matlab has been incorporated in the algo-rithm that receives a video file as input and automatically de-composes it to its frames at the output. Output frames followthe appropriate format so as to fit to the input requirements ofthe following training state. Frames are numbered and stored toa predefined file. The required time is in the order of few sec-onds.

The second step includes the definition of training examples,both positive and negative. Positive are training examples thatbound moving objects, whereas examples that do not includemoving objects are declared as negative. Positive and negativeexamples can either be true or false. According to this part oftraining, a number of true positive examples are selected at var-ious scales in successive frames. This enables the algorithm tofind all possible scales of moving objects. For example, as faras the first experiment concerns, 250 positive and 250 negativeexamples were selected. The larger the number of examples themore accurate the algorithm becomes, at the cost of time. Thesame method is applied in order the algorithm to become able todistinguish negative examples, i.e., to decide effectively whethera specific part of a frame corresponds to a moving object or toa static background. The computed time for this step does notexceed a few decades of minutes.

As soon as that process is completed, the third preliminarystate takes place, which contains the generation of all filters andit is accomplished in a few seconds. Two types of filters aregenerated, appearance and motion filters. Four basic kinds ofappearance filters are employed with two, three, four, and sixrectangles respectively. Besides, there are three categories ofmotion filters. The first is generated from the sums of absolutedifferences between delta image and one of :

(6)

where and is the response of a singlesum-of-pixel filter.

The second type of motion filters originates from appearancefilters, but using one of image:

(7)

where is one of the sum-of-pixels filter used for appear-ance.

The third category is based on the magnitude of motion in oneof the difference images:

(8)

where is the response of a single sum-of-pixel filter.


The final state of this preliminary process is completed withthe training of the classifiers and its validation. It is a processthat may last long, e.g. several hours. Training involves selectinga subset of features and assigning weights to them as well asassigning a threshold to each classifier. An important issue ishow to determine the optimal thresholds for a feature duringtraining. The method adopted is a simplification of the univariatequadratic discriminant analysis [38]. According to this analysis,the filter responses form Gaussian distributions for both posi-tive and negative examples. Then, the average of the weightedmeans of the negative and positive examples is assigned to fea-ture threshold. Hence, a threshold is chosen that approximatelyminimises the error. The cascade is formed by first trainingone large classifier and then inserting thresholds at intermediatepoints within the classifier. In order the number of weak classi-fiers to be decided, various tests take place. A common choiceis the whole classifier to consist of 40 weak classifiers. Interme-diate thresholds are chosen so that most negative examples arerejected, whereas most of the positive examples pass.

Another important elaboration on methodology is a processcalled “ad hoc filtering”. It has been adopted in order moving ob-jects detection to become more accurate and strict. It is appliedby taking into consideration specific characteristics of the back-ground and introducing additional rules that restrict the area ofdetection. For example, in case that certain regions of the frameare not of interest, they are blocked from being tested for pos-sible detection and tracking. In Fig. 7, for instance, the upperpart of the frame displayed can be assumed as not of interestand by adapting the corresponding instructions, the area of de-tection is confined. Moreover, further control to bounding boxesthat demonstrate detected moving object takes place, in the casethat these boxes share slight location differences. This results ineffective reduction of the number of bounding boxes that detecta single pedestrian in a frame. Consequently, detecting infor-mation becomes easier utilized and exploitable. The system canidentify the location of the pedestrians dynamically, i.e., fromany frame, whenever that appears and even more to match eachdetected person to a single pair of coordinates. Specifically, eachpedestrian is recognized with a single orthogonal bounding boxand the intersection point of the medial lines of this box (bluestar). In fact, ad hoc firmer filtering is an alternative approachto longer training with an increased number of weak classifiersand to longer threshold searching as well. The adoption of sucha method leads to a considerable decrease of video training andprocessing time as well as to the alleviation of computational re-sources. Otherwise, training process should be lengthened bothin time and complexity by increasing the number of training ex-amples and/or the number of weak classifiers. Fig. 7 clearly de-picts the effect of additional filtering in a frame.

Finally, in case that recording takes place with an angularoffset and/or from a relative low height then discrepancies mayoccur. In particular, the specification of pedestrians’ positionsoperated by the detection algorithm and the corresponding def-inition of initial conditions may severely diverge. Under suchconditions, direct application of the response of the detectionalgorithm as initialization input to the CA model introduces anerror as far as the proper spatial representation of the crowddistribution concerns. In order such a representation to become

Fig. 7. Effect of extra filtering in a frame. (a) Frame before the application ofextra filtering and (b) the same frame right after.

more accurate, rotation of coordinates and axis displacementare incorporated, as an intermediate, correction level betweenthe output of the tracking algorithm and the initial input of thepedestrian movement model. This process commences withthe conversion of the computed pedestrians’ coordinates fromCartesian to polar and continues by rotating axes anti-clock-wise. The precise rotation, in angular degrees, depends on theconditions of the experiment. Selected coordinates are trans-formed back to Cartesian and the whole procedure is completedwith axis displacement in both directions.

V. EXPERIMENTS

Preliminary assessment of the crowd management anticipa-tive system has taken place in regard to empirical data. Thesystem evaluates information in relevance to the optimum ca-pacity of the area in exits and activates sound and optical signalsthat redirect people to other exits. This leads to the early allevia-tion of a congested situation before it takes non-reversible char-acteristics. Empirical measurements are often restricted to den-sities up to 4–6 persons per square meter [13]. In the following,two experiments are thoroughly presented. They describe theoperation of the system and evaluate its performance as well.All computations took place on a 2.00 GHz Intel Core 2 pro-cessor incorporated in a general purpose computer with 2.00 GBRAM and supported by a NVIDIA GeForce Go 7440 graphicscard with 128 MB exclusive memory, extended up to 400 MB.


Fig. 8. Initialization of the pedestrian movement model. The transition from thefirst stage of the anticipative system, i.e., the detection algorithm to the secondone, i.e., the crowd movement model. Red-dotted areas correspond to areas ofinterest in front of exits.

The first experiment was carried out in an open place, aschoolyard and the participants were 23 pupils, at the age of15–17 years. According to the conditions of the experiment, allpupils were randomly distributed across the yard and as soonas the bell rang they moved towards two presumed exits at theends of the long side of the yard. In each case the exit pointwas supposed to be the margin between two chairs. No furtherinstructions were given, in order their decisions not to be influ-enced or even manipulated. Their motion was recorded underangular offset and from a considerable distance as well. Thefact that the people in the experiment shared similar physicalfeatures allowed further assumptions to be adopted. Hence, amaximum velocity equal to unity was formed, meaning thatmovements were only performed towards the closest neighbors.Furthermore, acceleration and braking time were considerednegligible and the CA grid homogeneous and isotropic.

As soon as the preparative process concerning the trainingof the detection and tracking algorithm was completed, thesystem proceeded to the initialization of the CA crowd move-ment model. As the system supports the dynamic definition ofthe initial conditions of the model, each frame of the recordedvideo could have been defined as initial. The anticipative systemprogressed to the second stage of its operation, modeling themovement of pedestrians and making a decision whether toalert (stage 3) or not on forthcoming congestion in front ofexit points. Fig. 8 illustrates the transition from the first stageof the detection algorithm to the second one, i.e., the crowdmovement model.

At this state, data processing time decreased, thus improvingthe decision making mechanism. More specifically, for theavailable computational resources and since all elaboratingprocedures completed, it took about 1 s to the tracking algo-rithm, to detect almost all pedestrians in a 720 576-pixelimage of the video file of the first experiment. Taking intoconsideration that the recording rate was 23.98 frames/s, therewas a delay between the real locations of individuals, as theywere expressed by a particular frame, and the response of thetracking algorithm. In this way, the system can be consideredas near real-time.

Moreover, for the given recording rate, due to the fact thatthe total number of frames of this experiment was equal to 215,the total time of the experiment was almost 9 s. The conges-tion limit of the left exit point (four pupils in front of the exit)was exceeded (and the alarm was activated) in frame 120, thatis after almost 5 s. It should be pointed out that the trackingalgorithm itself would require 120 s to reach the limited stateof congestion. This would cause severe delay and occlusion infront of the exit. Moreover, the response of the tracking algo-rithm would be downgraded due to the increased crowd den-sity, thus embarrassing the whole process. On the other hand,it took only 3.4 s to the route estimation CA model to reachthe same point, provided that it was initialized by the very firstframe of the video file. This corresponded to a gain of

(or gain in time response) com-pared to real experiment conditions and

compared to the responseof the tracking algorithm itself.

Under urgent circumstances such a reduction of processingtime can be proven significant. System administrator is pro-vided with extra time to react as well as with a preliminary il-lustration of the impending scenery and possible escaping outletroutes. Furthermore, the system contributes to the reduction ofthe number of surveillance cameras, restricting their use, for ex-ample, only in front of exits. The monitoring personnel are di-minished as well, thus allowing them to be charged in a more ac-tive role, as for instance that of crowd guiding crew. The crowdmovement model simulates the motion of pedestrians towardsexits. If the number of people inside the areas of interest ex-ceeds a predefined limit, then optical and sound signals are acti-vated and pedestrians may change their direction towards alter-native escaping points. The limit indicates the maximum allow-able congestion in front of an exit and it is set empirically, takinginto consideration the special conditions of each site. Fig. 9 il-lustrates alarm activation and change of direction in successiveframes picked within a very short time interval, during the firstexperiment.

The second experiment clarifies better the aforementioned ac-tivation. It took place in a teaching room, and the people thatparticipated were all students, with their age varying between18 and 23 years. The number of people was similar to that ofthe previous experiment, 25, but this time people entered theframe gradually. Following the principal conditions of the firstexperiment, all students were randomly distributed across theclassroom, where desks formed four rows, and as soon as thebell rang they moved towards exit A, located at the northeastcorner of the room. There was also one more exit in the room,


Fig. 9. (a)–(c) Three successive frames displaying alarm activation and pos-sible change of direction. For better readability, as soon as a blue star, i.e., apedestrian enters red-dotted areas of interest, it is surrounded by a grey circle. (d)The diagram depicts the affect of alarm activation. At time step 15, the numberof individuals inside the area of interest exceeds the limit of congestion (fourpeople), alarm is activated, individuals change direction and congestion insidethe area decreases below limit (time step 16).

located at the southeast corner of the room, but it was supposedto be closed. Once again, no further instructions were given.

The preliminary elaboration as described and discussed forthe case of the first experiment was repeated for the second ex-periment as well. Ad hoc filtering, focusing on distinct featuresof the background and adopting more specific, event-orientedrules further elaborated filtering process (Fig. 10). Videorecording was held from a close distance and with an angularoffset as well. Under such conditions, the angular offset turnedto be a serious matter as far the initialization process con-cerns and the corresponding elaborating process described in

Fig. 10. Effect of ad hoc filtering. A frame of the second experiment before (a)and after (b) ad hoc filtering implementation.

Section IV was applied. Specifically, Cartesian coordinatestransformed to polar and a rotation of 30 anticlockwise wasperformed. The rectifying procedure was completed with axisdisplacement equal to 150 pixels and 75 pixels to - and

-axis, respectively. The values preferred for the afore-men-tioned correction method were implied by the space allocationand were decided manually. Fig. 11 clearly depicts the pedes-trians’ coordinates shift and the initial conditions of the crowdmovement model. Primary coordinates are represented byblue stars bounded by a red box that spot locations arisen bythe response of the enhanced detection algorithm, whereasshifted coordinates are denoted by turquoise stars. Black rowscorrespond to rows of desks as located in the classroom and theorange-spotted area, near the exit, corresponds to the conges-tion sensitive area.

The completion of the enhanced initialization procedure de-notes the end of the first stage of the crowd management antic-ipative system and the onset of the second one, i.e., the simula-tion of the crowd movement and the estimation of the directionof motion of pedestrians in the very near future. According to thesimulation process of the model, as soon as the number of peoplethat appeared to the congestion-sensitive area exceeded the limitof congestion, sound and optical signals were activated, peopleinside this area changed their direction and they moved towardsthe second exit, the emergency one that opened simultaneously.The system, acting anticipatively, defined the emergency exit asthe optimum one in order to alleviate presumable forthcoming


Fig. 11. (a) Drift of the coordinates of the pedestrians and (b) the initial stateof the CA-based pedestrian movement model.

congestion in exit A. People outside the area of interest kept ap-proaching their initial target, without changing their destination.The recorded reaction of the pedestrians that participated in theexperiment affirms the response of the crowd movement simu-lation model. Indeed, people at longer distances from the exitignored the warning signal and retained their direction of mo-tion. Besides, such behavior can be rationally explained takinginto consideration that since people in front moved away fromexit A, the level of congestion dropped significantly. Thus, late-comers had free space enough to move forward (Fig. 12).

Regarding the runtimes of the second experiment, the re-sponse time of the tracking algorithm, in order to detect almostall pedestrians in a 720 576-pixel image was about 0.85 s. Therecording rate remained 23.98 frames/s and the total number offrames of this experiment was equal to 264. Hence, the totaltime of the experiment was about 11 s. The congestion limitwas exceeded in frame 168 (that is after 7 s). Consequently, thetracking algorithm itself would requireto reach the limited state of congestion. The route estimationCA model needed 4.2 s to reach that point, under the assump-tion of first frame initialization. This corresponded to a gainof (or ) comparedto real experiment conditions and (or

) compared to the relativeresponse of the tracking algorithm. All runtimes and relevant in-formation for both experiments are summarized in Table I.

In regard to the first experiment, the scenery of the secondexperiment included the existence of obstacles, i.e., desks andchairs, which deviated pedestrians’ routes towards the exit.Moreover, it took place in a bounded area, further restrictingmoving options. Fig. 12 also illuminates in a condense manner

Fig. 12. Four successive frames displaying response of individuals duringalarm activation. In frame (a), people move towards exit A, not having reachedthe area of surveillance yet. Alarm is activated in frame (b). Only pedestrians infront change direction (inside green dotted circle moving towards the directionof the green arrow) [frame (c)]. Pedestrians coming from the back keep movingtowards exit A [following the direction of the red arrow in frame (d)].

TABLE IRUNTIMES AND RELEVANT INFORMATION FOR BOTH EXPERIMENTS

the three stages that complete the processing framework ofthe decision support, anticipative system; motion simulation,


signal activation due to occlusion-prevention threshold excessand movement redirection.

VI. CONCLUSIONS AND FUTURE WORK

As far as crowd safety improvement concerns, a combina-tion of measures can be recommended. Part of them involvesconstructional changes of public facilities, regarding theirdesign and operation. Unfortunately, it is difficult proposedmodifications to take place in existing buildings or they concernonly construction principles of future facilities. On the otherhand, enhanced monitoring and crowd management systemshave been extensively applied. They allow surveillance butthey are restrictive as far as it concerns the amount of timethey provide to operators to react in order to prevent unpleasantcircumstances. Thus, it could be helpful the operation of a toolfor the estimation of possible pedestrian movements that wouldallow anticipative crowd management. Such approaches havenot been thoroughly employed so far. The proposed CA-basedcrowd management system is such an application. It is an activesystem that allows dynamic management of crowd congestionunder alternate conditions; it responds fast—in the order offew seconds—and provides more time of reaction in cases ofemergency.

The anticipative system that prevents escape points from oc-clusion under evacuation conditions has been thoroughly pre-sented. Detection and tracking of pedestrians initialize and con-trols dynamically a CA-based, crowd movement model that al-lows the estimation of possible route of individuals for the verynear future. Presumable congestion in exits during crowd egress,leads to the prompt activation of sound and optical signals thatguide pedestrians towards alternative and safer escaping desti-nations. This anticipative, decision-making approach providesfast elaboration of critical circumstances. It alleviates data pro-cessing and offers a preliminary illustration of the forthcomingscenery. Congestion threshold is adjustable providing compati-bility under various circumstances. The decision making mech-anism is provided with extra time and more options to reactunder different conditions. Furthermore, the system contributesto the reduction of the number of surveillance cameras, and pro-vides to the personnel in charge a more active role in cases ofemergency.

The CA model and the tracking algorithm can be imple-mented following FPGA logic. Hardware would acceleratethe response of the model by exploiting parallelism in CAstructures. The incorporation of the design in an embeddedsystem dedicated to surveillance would be advantageous interms of compactness and low cost. Furthermore, in cases ofoffsets during recording, a heuristic method can be developedthat would automatically adjust the initial conditions of the CAmodel that dynamically derive from the tracking algorithm.Besides, the consistency of the anticipative crowd managementsystem can be enhanced by testing it in cases of high densityand under panic conditions.

Finally, occlusion handling is an issue of major concern,since occlusion causes various ambiguities. When objects areoccluded by fixed objects, some resolution is possible throughmotion region analysis and partial matching. However, in

cases of self-occlusion or when multiple moving objects sharesimilar features and occlude each other, detection and trackingprocess is severely embarrassed. Future work focuses on theimprovement of the detection and tracking algorithm by incor-porating Bayesian classification techniques to cope with imagesegmentation and the estimated position of each person alongwith data fusion from multiple cameras. Data received fromdifferent cameras could be combined aiming at the probabilisticdetermination of the location of a moving object. Thus, thereduction of errors occurring while tracking multiple people ina cluttered scene could become feasible.

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Ioakeim G. Georgoudas received the Dipl. Eng.degree in electrical and computer engineering fromthe Aristotle University of Thessaloniki, Greece,in 2000 and the M.Sc. degree in Microwaves andOptoelectronics from the Department of Electronicand Electrical Engineering, University CollegeLondon (UCL), University of London, U.K., in2001. Currently, he is pursuing the Ph.D. degree inthe Department of Electrical and Computer Engi-neering, Democritus University of Thrace, Greece.His research interests include large scale complex

systems, cellular automata applications and electronic systems.

Mr. Georgoudas is a member of the Technical Chamber of Greece (TEE). Heis a teacher of Informatics in Greek State Secondary Education.

Georgios Ch. Sirakoulis (M’95) received the Dipl.Eng. and Ph.D. degree in electrical and computer en-gineering from the Democritus University of Thrace(DUTh), Greece, in 1996 and 2001, respectively.

He is an Assistant Professor in the Departmentof Electrical and Computer Engineering, DUTh.He has published more than 80 technical papersand he is co-editor of one book and co-author oftwo book chapters. His current research emphasisis on automated electronic systems design, cellularautomata theory and applications, CAD systems,

applied electronics, bioelectronics and molecular electronics.Dr. Sirakoulis received a prize of distinction from the Technical Chamber

of Greece (TEE) for his Diploma Thesis in 1996 and he was also foundingmember and Vice President of the IEEE Student Branch of Thrace for the pe-riod 2000–2001. He is Associate Editor of the Recent Patents on Electrical En-gineering and member of the TEE, IEEM, IET, EGU, and ISCB.

Ioannis Th. Andreadis received the DiplomaDegree from the Department of Electrical andComputer Engineering, Democritus University ofThrace, Greece, in 1983 and the M.Sc. and Ph.D.Degrees from the University of Manchester, U.K., in1985 and 1989, respectively.

He is currently with the Democritus Universityof Thrace. His research interests are mainly inelectronic systems design, intelligent systems andmachine vision. In these areas he has publishedsome 200 referred publications in book chapters,

international journals and conferences.Dr. Andreadis was awarded the IET Image Processing Premium in 2009. He

received the best paper award (Computer Vision & Applications) in PSIVT 2007as well as the best paper award in EUREKA 2009. He is author of an Institute ofPhysics (IOP) “Select Paper” in 2010. Professor Andreadis is a member of theBoard of Governors of the European Commission Joint Research Center. He isa Fellow of the Institution of Engineering and Technology, an Associate Editorof the Pattern Recognition Journal and a member of the Technical Chamber ofGreece.

an anticipative crowd management system preventing clogging in exits during pedestrian evacuation...

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