1. Target Tracking In a Scene By Saurabh Mahajan Supervisor Dr. R. Srivastava B.E. Project

2. Definition of Tracking Tracking – Generate some conclusions about the motion of the scene, objects, or the camera, given a sequence of images – Knowing this motion, predict where things are going to project in the next image, so that we don’t have so much work looking for them. Normal Detection and recognition are expensive

3. Applications -Traffic Control -Surveillance -Weapon Guidance -Personalized Sports Training -Clinical Studies of Orthopedic Patients

4. Tracking – Wide Field Offline Real Time Single Object, Multiple Objects Single Stationary Camera, Multiple Cameras, Moving Cameras General Object Tracking, Human Motion Tracking and Analysis

5. Assumptions Single Camera Camera is Still Offline, not real time Single object Tracking

6. Flowchart of the Tracking System

7. Preprocessing Extracting individual frames Use of spatial filters – uniform filter Data Format - grayscale

8. Preprocessing Original Frame Image after Smoothing

9. Background Modeling Basic Background System Median Filter- use of buffer Gaussian Distribution

10. Background Modeling Original Frame Image after Smoothing a) Subtracted ImagePrevious frame at time t-1 Current frame at time t

11. Background Modeling Median Filtered Frame Better Subtracted Image Current frame at time t

12. Morphological Processing Opening --- erosion followed by dilation 8 Connected Grouping

13. Morphological Processing Dilation is defined as Erosion is defined as k

14. Morphological Processing Original Image Image after Opening

15. Object Blobbing Representation of Shape using a) Bounding Rectangular Box b) Silhouette Extraction c) Chain codes Approximate Position – Centroid

16. Object Blobbing

17. Tracking Original Frame Traced Object

18. Kalman Filters Best estimate of state variables given - behavior of the system - measurements Kalman Filter

19. Conclusion And Future Work Active field of research Encouraging Results Multiple Target Tracking 3D Motion Human Articulate Kinematic Model

20. References [1] Ching.S,Cheung.S and Kamath.C “Robust Techniques for Background Subtraction in Urban Traffic Video” EURASIP journal on applied signal processing Volume 2005 (2005), Issue 14, Pages 2330-2340 [2] Mikic I., Trivedi M., Hunter E. and Cosman E. “Human Body Model Acquisition and Tracking Using Voxel Data” International Journal of Computer Vision 53(3), 2003, Pages 199–223 [3] A. J. Lipton, H. Fujiyoshi, and R. S. Patil. Moving target classification and tracking from real-time video. In Proc. IEEE Image Understanding Workshop 1998, pages 129—136 [4]Gonzalez R.C and Woods R.E “Digital Image Processing”,Pearson Education, Seventh Indian Print, 2004 [5] J. Nascimento and J. S. Marques. “New Performance Evaluation Metrics for Object Detection Algorithms”. In IEEE Workshop on Performance Analysis of Video Surveillance and Tracking (PETS’2004), May 2004. [6] Pnevmatikakis A.,Polymenakos L., “2D Person Tracking using Kalman Filterring and Adaptive Background Learning in a Feedback Loop”,Clear 2006 [7] www.cvg.rdg.ac.uk/slides/pets.htmlwww.cvg.rdg.ac.uk/slides/pets.html [8] http://www.innovatia.com/software/papers/kalman.htmhttp://www.innovatia.com/software/papers/kalman.htm