Moving Object Extraction Team 12

1. Moving Object ExtractionTeam 12 Team Members Hari Kishan Bandaru , Sneha AnandYeluguri, V S P V S K Kumar Parimi

2. Overview • Moving Object Detection Problem • Classification • The Basic Methods • Conclusion • References

3. Moving Object Detection Problem • Moving Object Detection Problem • Detecting Moving objects from a video sequence of either a fixed or a moving camera. • Static Camera • Moving Camera • At present there exist three methods to detect moving targets, • Optical flow method • Consecutive Frames Subtraction • Background Subtraction

4. Contd… • 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

5. Classification – Based on camera • 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 flow • Moving direction of feature • Length of moving direction • Decide region of foreground object

6. Contd… • 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

7. Classification of methods • 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

8. Classification of methods • 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.

9. Classification of methods • 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

10. Classification of methods • These methods could be roughly divided into two approaches. • 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 • Methods of the second approach generate background models of each small patch in images using features robust to changes in luminance. • 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

11. Classification of methods • 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.

12. Classification of methods • Basic BGS – Example :

13. Classification of methods • 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.

14. Classification of methods • 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.

15. Classification

16. Classification

17. Classification of methods • “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.

18. Classification of methods • “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.

19. Classification of methods • “Moving Object Detection with Background Model based on Spatio-Temporal Texture” addressesa 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.

20. Conclusion • 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.

21. References • 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 • WenmingYang; 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 • RenMing-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

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