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Object Tracking

Object Tracking. Group M Sujith Thomas (Y7111037) Surya Prakash (Y7111063). Problem Definition. To track an object using Silhouette Tracking. Input will be a video Output will again be a video with the tracked object being marked by a red contour. Motivation.

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Object Tracking

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  1. Object Tracking Group M Sujith Thomas (Y7111037) Surya Prakash (Y7111063)

  2. Problem Definition • To track an object using Silhouette Tracking. • Input will be a video • Output will again be a video with the tracked object being marked by a red contour.

  3. Motivation • Developing a simple and fast technique to track an object across frames in the video. • Color histogram could be a stable object representation that works well for varying illumination conditions.

  4. Literature Referred • Alper Yilmaz, Omar Javed, M. Shah, “Object tracking: A survey,” ACM Computing Surveys (CSUR) , 38(4),2006. • Michael J. Swain,Dana H. Ballard,“Color Indexing”, International Journal of Computer Vision, 1991. • David Forsyth and Jean Ponce, Computer Vision: A modern Approach, Prentice Hall India 2004

  5. Our Approach • Object Representation • Initial Contour in first frame • Color Histogram generation • Noise removal from the color Histogram

  6. Our Approach Contd.. • Contour Evolution • State of the contour defined by the position of its centroid, velocity and acceleration • Next state is predicted based on the current state • [Stochastic Search] Best contour selected by searching the neighborhood stochastically based on Sigma value of the errors. • [Kalman Filtering] After the new position is found, error in prediction is obtained and the modeling parameters are updated.

  7. Result-1

  8. Result-2

  9. Result-3 Ref: The VIVID Tracking Evaluation Web Site, CMU http://www.vividevaluation.ri.cmu.edu/main.html

  10. Time Taken in Processing • For Result-3 • Processing time = 0.23 Seconds/ Frame (Excluding disk input-output time)

  11. Conclusions • Representing objects using a color histogram gives good results when we require fast tracking of objects. This is because it is computationally inexpensive. • Method presented can track the object even when a few frames are skipped. • This approach can take care of partial occlusion of object. • Approach fares well only when object color is atleast slightly different from that of the background.

  12. Thank You

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