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Team MembersHari Kishan Bandaru ,Sneha Anand Yeluguri,V S P V S K Kumar Parimi

Moving Object Detection ProblemClassificationThe Basic MethodsConclusionReferences

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Moving Object Detection Problem

Detecting Moving objects from a video sequence of either a fixed or a moving camera.

1. Static Camera2. Moving Camera

At present there exist three methods to detect moving targets,

Optical flow method Consecutive Frames Subtraction Background Subtraction

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Applications : Video surveillance systems, Traffic monitoring,Human motion capture,Aerial and Ground sensors, Situational Awareness, Day or night operations, Detection and tracking while moving, Border protection and monitoring, intelligent transportation, intrusion surveillance, airport safety

Moving Object Detection using static camera Detecting moving objects from a video sequence of a

static camera. Background – static Foreground – moving objects

Moving Object Detection using moving camera Find good feature to track Track features Classify foreground and background feature points:

Optical flowMoving direction of featureLength of moving direction

Decide region of foreground object

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Affine motion model for background registration: <image>

The affine model describes the vector at each point in the image.

Need to find values for the parameters that best fit the motion present

Point feature tracker for correspondence between frame pairs

Iterative reweighted least squares to avoid the features in moving objects

Optical flow method : complex and bad anti-noise performance can not be applied to real-time processing without

special hardware device.

Consecutive Frames Subtraction : Is a simple operation, realizes easily and has strong

adaptability on the dynamic changes in the environment. can not be completely extracted moving targets

Background Subtraction : Detect the moving objects as the difference between the

current frame and the image of the scene background. To detect moving objects, each incoming frame is compared

with the background model learned from the previous frames to divide the scene into foreground and background.

It is widely used in many surveillance systems. Advantage : does not require previous knowledge of moving

objects such as shapes or movements. Disadvantage : cannot discriminate moving objects from

backgrounds when these backgrounds change significantly.

Background images must adapt to : Illumination changes, Distraction motions ( camera

shake, moving tree, ocean waves, etc), shadows, bad weathers, etc.

Background Subtraction methods Basic BGS Running Gaussian average Mixture of Gaussians Enhanced mixture of Gaussians Kernel Density Estimators Mean-shift based estimation Combined estimation and propagation Eigen backgrounds

These methods could be roughly divided into two approaches.

1. Methods of the first approach generate background models of each pixel, which is the minimum component of an image, using statistical distribution based on backward observation

2. Methods of the second approach generate background models of each small patch in images using features robust to changes in luminance.

3. There is a method for generating a background model which uses a combination of a pixel-wise background model and a patch-wise background model

Basic BGS : Pixels belongs to foreground if

| Current Frame – BG Image| > Threshold BG image can be just the previous frame or the average

image of a number of frames Works only in particular conditions of objects’ speed and

frame rate Very sensitive to the threshold At each new frame , each pixel is classified as either

forground or background . If the pixel is classified as foreground , it is ignored in

the background model.

Basic BGS – Example :

Basic BGS limitations :

They do not provide an explicit method to choose the threshold.

Based on a single value , they cannot cope with multiple modal background distributions.

Mixture of Gaussians

is the way to cope with multi modal background distributions.

Mixture of Gaussians actually models both the foreground and the background.

Region based mixture of Gaussians is one of the best methods for Moving Object Detection with Distraction Motions.

Optical Flow method

Consecutive Frames

Subtraction

Background Subtraction

Automatic moving Object Extraction using X-Means Clustering

Efficient Spatio-temporal segmentation for extracting moving objects in video sequences

Moving Object Detection with Background Model based on Spatio-Temporal Texture

•Complex and bad anti-noise performance•cannot be applied to real-time processing without specialhardware device

•simple operation, realizes easily •has strong adaptability on the dynamic changes in the environment

•provides a moving object comprehensive and reliable Information•very sensitive to the irradiation which is caused by dynamic scene changes•Advantage of not requiring previous knowledge of moving objects such as shapes or movements•Disadvantage :cannot discriminate moving objects from backgrounds when these backgrounds change significantly

Background SubtractionGenerate background models of each pixel

Generate background models of each small patch in images using features robust to changes in luminance

Generate a background model using combination of a pixel-wise and a patch-wise background model

Advantage : These pixel-wise background models have an advantage of covering frequent changes in the pixel grayscale in local areas in images, such as waving of tree branches or fluctuations of water.

Advantage : have an advantage in that features in small patches represent textures robustly within the patch and they can cover global changes in images such as transitions of sunlight or adjustments of ceiling lightluminance.

Advantage : reduces false positives using conjunction of detection results of a pixel-wise method and a patch-wise method

Disadvantage :have a defect in that they hardly follow rapid translations of a pixel grayscale well across a global area in images, such as transitions of sunlight or adjustments of ceiling light luminance, because statistical distributions of each pixel grayscale belonging to the background translate substantially in a short time according to that kind of changes.

Disadvantage :have a defect in that they hardly follow rapid and intensive changes within image patches such as waving of tree branches or fluctuations of water, because textures within an image patch changes substantially according to that kind of changes

Disadvantage : this combinatorial method has a redundancy in that it executes two procedures of signal processes for one region in images.

“AUTOMATIC MOVING OBJECT EXTRACTION USING X-MEANS CLUSTERING” paper deals with moving object detection and extraction and is an example for optical flow method. A moving object extraction method based on region merging

and that can automatically determine the number of extracted objects has been proposed

Steps in Moving Object Extraction Using X-Means Clustering Region Segmentation by the Watershed Algorithm Feature Point Selection and Motion Estimation X-means Clustering and Region Labeling

Once the feature points are selected, affine motion parameter for each feature point is estimated.

Feature points are clustered by X-Means clustering for estimated affine motion parameters.

Finally , a label is assigned to the segmented region obtained by the morphological watershed algorithm.

The label is decided by voting for the feature point cluster in each region. Labeling result represent moving object extraction.

“Efficient Spatio-temporal segmentation for extracting moving objects in video sequences”, this paper addresses a scheme to extract moving object from video sequences using the Consecutive frame subtraction method.

Here Each frame is decomposed into blocks

By taking the difference of two consecutive block images ,gives the binary mask which determines the moving blocks.

“Moving Object Detection with Background Model based on Spatio-Temporal Texture” addresses a method for detecting moving objects with a background model that covers dynamic changes in backgrounds using a spatio-temporal texture which describes motion in addition to appearance.

This proposed method can cover global changes in images by using appearance information similar to other conventional spatial textures.

In addition, it can cover local changes in images by using motion information, although local changes are difficult to cover for conventional patch-wise models which use only appearance information.

We have tried to classify various methods for moving object detection and extraction.

Each classification has its own advantages and disadvantages and can be used based on various conditions and scenarios.

Imamura.K, Kubo.N, Hashimoto.H,"Automatic moving object extraction using x-means clustering Picture Coding Symposium (PCS),pp246 - 249 , Dec 2010.

Li, Qing-Zhong, He, Dong-Xiao, Wang, Bing "Effective Moving Objects Detection Based on Clustering Background Model for Video Surveillance“ Image and Signal Processing,vol.3,pp.656-660,2008

Wenming Yang; Jilin Liu; Huahua Chen "Automatic extraction of moving objects in video sequences based on Spatio-temporal information“ Annual Conference of IEEE on Industrial Electronics Society on pp. 4,2005

Ren Ming-yi; Li Xiao-feng; Li Zai-ming,"Moving objects extraction from video sequences based on GMM and watershed“ International Conference on Communications, Circuits and Systems,pp.525 - 529 ,2009

R. Li, S. Yu, and X. Yang, "Efficient spatio-temporal segmentation for extract ing moving objects in video sequences," IEEE Transactions on Consumer Electronics, vol. 54, pp. 1161-1 167, Mar 2007 .

Kavitha, G.; Chandra, M.D.; Shanmugan, J."Video object extraction using model matching technique: a novel approach“ 14th International Workshop on Multimedia Communications and Services,pp.118-121,2007

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Yumiba, R.; Miyoshi, M.; Fujiyoshi, H."Moving object detection with background model based on spatio-temporal texture " IEEE Workshop onApplications of Computer Vision (WACV), pp.352 - 359 ,2011.

Xiaoyan Zhang; Yong Shan; Wei Wei; Zijian Zhu,"An Image Segmentation Method Based on Improved Watershed Algorithm“ International Conference on Computational and Information Sciences (ICCIS),pp.258-261,2010

Dewan, M.; Hossain, M.J.; Chae, O."Segmentation of moving object for content based applications",International Conference on Consumer Electronics,pp.1-2,2009

Mofaddel, M.A.; Abd-Elhafiez, W.M.,"Fast and accurate approaches for image and moving object segmentation",International Conference on Computer Engineering & Systems,pp.252 - 259 ,2011

Xuehua Song; Jingzhu Chen; Chong He; Xiang Zhou,"A Robust Moving Objects Detection Based on Improved Gaussian Mixture Model“ International Conference on Artificial Intelligence and Computational Intelligence (AICI), vol.2,pp.54 - 58 ,2010

Xiong Weihua; Xiang Lei; Li Junfeng; Zhao Xinlong "Moving object detection algorithm based on background subtraction ",30th Chinese on Control Conference,pp.3273 - 3276 ,2011