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Detecting and Tracking Tractor-Trailers Using View-Based Templates

Detecting and Tracking Tractor-Trailers Using View-Based Templates. Masters Thesis Defense by Vinay Gidla Apr 19 ,2010. Introduction. Object tracking: Sports analysis Games and gesture recognition Retail video mining Automobile driver assistance Traffic surveillance

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Detecting and Tracking Tractor-Trailers Using View-Based Templates

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  1. Detecting and Tracking Tractor-Trailers Using View-Based Templates Masters Thesis Defense by Vinay Gidla Apr 19,2010

  2. Introduction • Object tracking: • Sports analysis • Games and gesture recognition • Retail video mining • Automobile driver assistance • Traffic surveillance • Volume, individual speeds, classification • Lane changes, speed violations, congestions

  3. Feature-based vehicle tracking • Beymer et al. 1997 use feature point approach with motion cues to segment vehicles using homography • Kanhere et al. 2008 use features with 3D estimation using multi-level homographyFeature_based.avi • Drawback: These approaches track features on the vehicle, not vehicle as a whole

  4. Template-based tracking • Model the object by 2D template of image intensities • Compare search image with template image • Comparison usually by discrete cross-correlation • Good: Both spatial and appearance information Able to retrieve shape of the object • Bad: Encode vehicle appearance from single viewpoint Do not adapt to changes in appearance of object

  5. Proposal • Overcome the limitations of a single template by using a template sequence instead of a single template • The template sequence encapsulates all of the vehicle’s perspective deformations • As a starting step, aim to detect and track contours of tractor-trailers in multi-lane traffic

  6. Video Sequences

  7. Template creation Training sequence: • A portion of traffic video containing a tractor-trailer • Process the video frames to create a template sequence

  8. Training Sequence

  9. Training frame

  10. Manual contour selection

  11. Template creation

  12. Template sequence

  13. Algorithm Overview

  14. Step 1: Background subtraction

  15. Reference background

  16. Input Video Frame

  17. Background subtracted frame

  18. Step 2: Blob-Template match

  19. Blob-Template match

  20. Plot of Blob-Template match

  21. Step 3: Trace contour

  22. Results based on template-blob correlation

  23. Plot of misalignment

  24. Gradient magnitude match • Reduce the misalignment by including salient features such as points of high gradient magnitude • These points are located at identical spatial locations in every tractor-trailer

  25. Training frame

  26. Gradient Magnitude

  27. Template Gradient Magnitude

  28. Template Gradient Sequence

  29. Results based on template-frame gradient match

  30. Plot of misalignment

  31. Test sequences

  32. Results(Lane 3)

  33. Results(Lane 2)

  34. Level set-based trackingfor automatic template generation

  35. Conclusion • The new approach accurately traces the contours of all the tractor-trailers in the traffic video • Works for multi-lane traffic • Minor misalignment

  36. Future extensions • Tracking other classes of vehicles such as passenger cars, buses etc • Compact template sequence with minimal template redundancy • Implement matching using level set techniques

  37. Thank You

  38. Questions & Discussion

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