reference independent moving object detection an

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B. Apolloni et al. (Eds.): KES 2007/WIRN 2007, Part I, LNAI 4692, pp. 501–509, 2007.

© Springer-Verlag Berlin Heidelberg 2007

Reference Independent Moving Object Detection: An

Edge Segment Based Approach

M. Ali Akber Dewan, M. Julius Hossain, and Oksam Chae*

Department of Computer Engineering, Kyung Hee University,

1 Seochun-ri, Kiheung-eup, Yongin-si, Kyunggi-do, South Korea, 449-701

[email protected], [email protected], [email protected]

Abstract. Reference update to adapt with the dynamism of environment is one

of the most challenging tasks in moving object detection for video surveillance.

Different background modeling techniques have been proposed. However, mostof these methods suffer from high computational cost and difficulties in

determining the appropriate location as well as pixel values to update the

background. In this paper, we present a new algorithm which utilizes three most

recent successive frames to isolate moving edges for moving object detection. It

does not require any background model. Hence, it is computationally faster and

applicable for real time processing. We also introduce segment based

representation of edges in the proposed method instead of traditional pixel

based representation which facilitates to incorporate an efficient edge-matching

algorithm to solve edge localization problem. It provides robustness against the

random noise, illumination variation and quantization error. Experimental

results of the proposed method are included in this paper to compare with some

other standard methods that are frequently used in video surveillance.

Keywords: Video surveillance, reference independent, chamfer matching,

distance image, motion detection.

1 Introduction

Automatic detection of moving objects is a challenging and essential task in video

surveillance. It has many applications in diverse discipline such as automatic video

monitoring system, intelligent transportation system, airport security system and so

on. Detail review on moving object detection algorithms can be found in [1] and [2].

Background subtraction based methods are the most common approaches that are

used for moving object detection. In these methods, background modeling is an

important and unavoidable part to accumulate the illumination and other changes in

the background scene for proper detection [3]. However, most of the background-

modeling methods are complex in computation and time-consuming for real time

processing [4]. Moreover, most of the time it suffers from poor performance due tolack of compensation with the dynamism of background scene [5].

Edge based methods are robust against illumination change. In [6] and [7], edge

based methods are proposed for moving object detection which utilizes double edge

* Corresponding author.



502 M.A.A. Dewan, M.J. Hossain, and O. Chae

maps. In [6], one edge map is generated from difference image of background and

current frame, I n. Another edge map is generated from difference image of I n and I n+1.

Finally, moving edge points are detected by applying logical OR operation on these

two edge maps. However, due to illumination change and random noise [6] in

background scene, false edge may appear in the first edge map and hence causes falsedetection in the final detection result. In [7], first edge map is computed from the

difference image of I n-1 and I n, and similarly second map is obtained from I n, and I n+1.

Finally, moving edges of I n are extracted by applying logical AND operation on these

two edge maps. However, because of noise and illumination change, edge pixels of an

edge map may be displaced little bit as compared to previous one. So, exact matching

through AND operation extracts scattered edge pixels, which fails to represent reliable

shape of moving objects. Moreover, pixel based processing for moving edge detection

is not feasible in terms of computation. A pseudo-gradient based moving edge

extraction method is proposed in [8]. Though this method is computationally fasterbut its background is not updated to take care of the situation when a moving object

stops its movement in the scene. In this situation, stopped object is continuously

detected as moving object. As no background update method is adopted in this

method, it is not much robust against illumination change. Additionally, this method

also suffers from scattered edge pixels of moving objects.

(a) (b) (c) (d)

Fig. 1. Difference between pixel based and segment based matching. (a) Edge image at time t ;

(b) Edge image of same scene at time t+1; (c) Result obtained by pixel based matching; (d)

Result obtained by segment based matching.

Considering the above-mentioned problems, we present an edge segment based

approach which utilizes three successive frames for moving object detection. In our

proposed method, two difference image edge maps of three successive frames are

utilized to extract moving edges instead of using edge differencing approach. It makes

the system robust against random noise as well as illumination variation. Since the

proposed method does not require any background model for detection, it is

computationally faster and efficient. Moreover, use of most recent frames, embodying

the updated information helps to reduce false detection effectively. In our proposed

method, the difference image edge maps are represented as segments instead of pixels

using an efficiently designed edge class [9]. An edge segment consists of a number of consecutive edge pixels. This novel representation helps to make the decision on

matching or in any other operations based on entire edge segment rather than an

individual pixel. This representation of edge provides the following benefits:



Reference Independent Moving Object Detection: An Edge Segment Based Approach 503

a) It facilitates to incorporate an efficient and flexible edge-matching algorithm [10]

in our proposed method which reduces the computation time significantly.

b) This type of representation facilitates our method to take decision about a

complete edge segment at a time instead of an individual edge pixel to keep or

discard it from the edge list during matching. Fig. 1 illustrates the advantages of segment based matching over pixel based matching. Here, pixel based matching

missed 20% edge pixels due to variation of edge localization in different frames.

Segment based matching does not suffer from this problem as it consider all the

points of a segment together. As a result, it reduces the occurrence of scattered

edge pixels in the detection result.

Since moving object segmentation is a separate problem from detection in video

surveillance, we have not considered it in our proposed method. However, because of

segment based representation of edges, our proposed method is able to extract reliableshape information of moving objects. Incorporating this shape information with image

segmentation algorithm, it is possible to segment out moving objects from current

image efficiently. Segment based representation also makes it possible to incorporate

knowledge to edge segments which can facilitate the higher level processing of video

surveillance such as tracking, recognition, human activity recognition and so on.

2 Description of the Proposed Method

The overall procedure of the proposed method is illustrated in Fig. 2. Detaildescription of our method is given in the following subsections.

1 1n n nD I I

− −= −

1n n nD I I

+= −1 1

( * )n n

DE G Dϕ − −

= Δ ( * )n n

DE G Dϕ = Δ

nI

1nI

− 1nI

+

Fig. 2. Flow diagram of the proposed method

2.1 Computation of Difference Image Edge Maps

Simple edge differencing approach suffers a lot with random noise. This is due to the

fact that the appearance of noise created in one frame is different from its successive

frames. This results in change of edge locations to some extent in successive frames.

Hence, instead of using simple edge differencing approach, we utilize difference

image for moving edge detection. Edges extracted from difference image are noise

robust, comparatively stable and hence partially solve the edge localization problem.

Two difference image edge maps are utilized in our proposed method for moving

object detection. To compute difference image edge maps, we compute two difference

images, Dn-1, and Dn utilizing three successive frames I n-1, I n, and I n+1 as follows:




1n n nD I I +

= − (1)

After computing Dn-1 and Dn, canny edge detection algorithm [11] is applied and

generates difference image edge maps, DE n-1 and DE n, respectively. In the difference

image edge maps, edge pixels are grouped together to represent as segments using anefficiently designed edge class [9]. To make the edge segments more efficient for

moving edge detection procedure, we maintain the following constrains during edge

segment generation:

a) If the edge segment contains multiple branches, then the braches are broken into

multiple edge segments from its branching point.

b) If the edge segment bends more than a certain limit at an edge point, the edge is

broken into two edge segments from that particular position.

c) If the length of a particular edge segment exceeds a certain limit, then the edge

segment is divided into a number of small edge segments of its permitted length.

Segment based representation helps the proposed system to use the geometric

shape of edges during matching for moving edge detection. It also helps to extract

solid edge segments of moving objects instead of extracting scattered or significantly

small edges. In this case no edge pixels are processed independently; rather all the

edge pixels in an edge segment are processed together for matching or in any other

operations. Fig. 3(d) shows the difference image edge maps generated from Fig. 3(a)

and Fig. 3(b). Similarly edge map in Fig. 3(e) is obtained from Fig. 3(b) and Fig. 3(c).

(a) (b) (c) (d)

(e) (f) (g)

Fig. 3. DT image generation and matching. (a) I n-1; (b) I n; (c) I n+1; (d) DE n-1; (e) DE n; (f) DT

image of DE n-1; (g) Edge matching using DT image. Here, Matching_confidence = 0.91287 .

2.2 Moving Object Detection

Edge maps, DE n-1 and DE n are used in this step to extract moving edges for moving

object detection in video sequence. DE n-1 contains the moving edges of I n-1 and I n, and

DE n contains the moving edges of I n and I n+1, respectively. Thus, the moving edges of




I n is common in both of the edge maps. Therefore, to find out moving edges, we

superimpose one edge map on another one and compute matching between them.

Hence, if two edge segments are of almost similar in size and shape, and situated

almost in same positions in the edge maps, then they are considered as moving edges

of I n. However, appearance of noise may cause slightly change of these parameters aswell. Hence, instead of exact matching, introducing some variability reduces

localization problem to obtain better results. Considering these issues, we have

adopted an efficient edge-matching algorithm in this proposed method, which is

known as chamfer ¾ matching [10]. According to the procedure of chamfer matching,

distance transform (DT ) image is generated from one difference image edge map and

then edge segments from another one are superimposed on it and compute matching

confidence. If the matching confidence is less than a certain threshold then the edge

segment is enlisted as moving edge. This threshold value gives the variability during

matching. In our method, we utilize DE n-1 to generate DT image and thereafter, edgesegments of DE n are superimposed on it to compute the matching confidence.

To compute DT image, we use integer approximation of exact Euclidean distance

to minimize the computation time [10]. Each pixel in DT represents the corresponding

distance to the nearest edge pixel in the edge map. In DT image generation, a two-

pass algorithm is used to calculate the distance values sequentially. Initially the edge

pixels are set to zero and rest of the position is set to infinity. The first pass (forward)

modifies the distance image as follows:

, 1, 1 1, 1, 1 , 1 ,min( 4, 3, 4, 3, )i j i j i j i j i j i jv v v v v v− − − − + −

= + + + + (2)

and thereafter, the second pass (backward) works as follows:

, , , 1 1, 1 1, 1 1, 1min( , 3, 4, 3, 4)i j i j i j i j i j i jv v v v v v+ + − + − + +

= + + + + (3)

where vi,j is the distance at pixel position (i, j). Fig. 3(f) illustrates a DT image which

is computed from difference image edge map shown in Fig. 3(d). In Fig. 3(f), distance

values of DT image are normalized into 0 to 255 for better visualization.

During matching, an edge segment of DE n is superimposed on DT image of DE n-1

to accumulate the corresponding distance values. A normalized average of these

values (root mean square) is the measure of matching confidence of the edge segment

in DE n, shown in following equation:

2

1

1 1_ [ ] { ( )}

3

k

i

i

Matching confidence l dist lk

=

= ∑ (4)

where k is the number of edge points in lth edge segment of DE n; dist(li) is the distance

value at position i of edge segment l. The average is divided by 3 to compensate for

the unit distance 3 in the chamfer ¾-distance transformation. Edge segments are

removed from DE n if matching confidence is comparatively higher. Existence of asimilar edge segments in DE n-1 and DE n produces a low Matching_confidence value

for that segment. We allow some flexibility by introducing a disparity threshold,

τ and empirically we set τ = 1.3 in our implementation. We consider a matching

occurs between edge segments, if Matching_confidence[l] ≤ τ . The corresponding




edge segment is considered as moving edge and consequently enlisted to the moving

edge list. Finally, the resultant edge list contains the edge segments of ME n that

belong to moving objects in I n. Fig. 3(g) illustrates the procedure of computing

matching confidence using DT image.

3 Experimental Results

Experiments have been carried out with several video sequences captured from indoor

as well as outdoor environment to verify the effectiveness of the proposed method.

We have applied our proposed method on video formats of size 640x520 and used

Intel Pentium IV 1.5 GHz processor and 512 MB of RAM. Visual C++ 6.0 and MTES

[12] have been used as of our working tools for implementation.

(a) (b) (c)

(d) (e) (f)

Fig. 4. (a) I 150; (b) I 151; (c) I 152; (d) DE 150; (e) DE 151; (f) Detected moving edges of I 151

Fig. 4 shows experimental result for moving object detection in an outdoor

environment. In this case, three consecutive frames, I 150, I 151 and I 152 shown in

Fig. 4(a), Fig. 4(b) and Fig. 4(c), respectively, are used to compute two difference

images D150 and D151. Thereafter, difference image edge maps, DE 150 and DE 151 shown

in Fig. 4(d) and Fig. 4(e), are computed using D150 and D151, respectively. Fig. 4(f)

shows the detected moving edges of I 151 which were common in both of the difference

image edge maps, DE 150 and DE 151.Fig. 5 shows another experimental result obtained with an indoor video sequence.

Fig. 5(a), Fig. 5(b), Fig. 5(c) and Fig. 5(d) show the background and three successive

frames I 272, I 273, and I 274, respectively, with different illumination condition and

quantization error. Result obtained from the method of Kim and Hwang [6] is shown

in Fig. 5(e), where double edge maps have been utilized to detect moving edges. In




their method, the difference between background and current frame incorporates most

of the noise pixels. Fig. 5(f) shows the result applying the method proposed by Dailey

and Cathey [7]. Result obtained from this method is much robust against illumination

changes as it uses most recent successive frame differences for moving edge

detection. However, it suffers from scattered edge pixels as it uses logical AND operation in difference image edge maps for matching. Illumination variation and

quantization error induces edge localization problem in difference image edge maps.

As a result, some portions of the same edge segment are matched and some are not,

and produce scattered edges in final detection result. Our method does not experience

this problem because of applying flexible matching between difference image edge

maps containing edge segments. The result obtained from our proposed method is

shown in Fig. 5(g).

(a) (b) (c) (d)

(e) (f) (g)

Fig. 5. (a) Background; (b) I 172; (c) I 173; (d) I 174; (e) Detected moving edges of I 173 using Kim

and Hwang method; (f) Detected moving edges of I 173 using Dailey and Cathey method; (g)Detected moving edges of I 173 using our proposed method

Table 1. Mean processing time in (ms) for each of the module

Processing steps Mean time (ms)

Computation of difference images 5

Edge map generation from difference images 39

DT image generation 11Computation of matching confidence andmoving edge detection

19

Total time required 74




In order to comprehend the computational efficiency of the algorithm, it should be

mentioned that with the processing power and the processing steps described above,

execution time for the moving object detection on grayscale images was

approximately 74 ms. Therefore, the processing speed was around 13 frames per

second. However, using computers with higher CPU speeds which are available thisday and in future as well, this frame rate can be improved. Table 1 depicts

approximate times required to execute different modules of the proposed method.

4 Conclusions and Future Works

This paper presents a robust method for moving object detection which does not

require any background model. Representation of edges as segments helps to reduce

the effect of noise and, incorporates a fast and flexible method for edge matching. So,

the proposed method is computationally efficient and suitable for real time automated

video surveillance system. Our method is robust against illumination changes as it

works on most recent successive frames and utilizes edge information for moving

object detection. However, the presented method is not very effective in the case of

detecting object with very slow movement as it uses three consecutive frames instead

of any background model. The extracted moving edge segments using our proposed

method represent very accurate shape information of moving object. These edge

segments can be utilized for moving object segmentation. Currently we are pursuing

moving object segmentation from moving edges utilizing watershed algorithm. As

segment based representation provides us with shape information of moving object,the proposed method can be easily extended for tracking, recognition and

classification of moving object. Experimental results and comparative studies with

respect to some other standard methods justify that the proposed method is effective

and encouraging for moving object detection problem.

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reference independent moving object detection an

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