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Tracking and Motion

Tracking and Motion. 정보산업공학협동과정 정우정. Contents. The Basics of Tracking Corner Finding Subpixel Corners Optical Flow Mean-Shift and Camshift Tracking Motion Templates Estimators The Condensation Algorithm. example. … /opencv/samples/c/ lkdemo.c (optical flow)

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Tracking and Motion

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  1. Tracking and Motion 정보산업공학협동과정 정우정

  2. Contents • The Basics of Tracking • Corner Finding • Subpixel Corners • Optical Flow • Mean-Shift and Camshift Tracking • Motion Templates • Estimators • The Condensation Algorithm

  3. example …/opencv/samples/c/ • lkdemo.c (optical flow) • camshiftdemo.c (mean-shift tracking of colored regions) • motempl.c (motion template) • kalman.c (Kalman filter)

  4. The Basics of Tracking • Understand the motion of object:identification and modeling • IdentificationTo finding the object of interest from the video stream. • ModelingProviding us with noisy measurement of the object’s actual position.

  5. Corner Finding (1/2) • Harris corner • Shi and Tomasi

  6. Corner Finding (2/2) image: input image (single-channel) eigImage, tempImage: scratch by the algorithm corners: result points after the algorithm corner_count: maximum number of points quality_level: minimal eigenvalue (0<x<1) mask: usual image block_size: pixel use_harris: Harris or Shi-Tomasi k: weighting coefficient

  7. Subpixel Corners (1/2)

  8. Subpixel Corners (2/2) image: input corners: initial guesses for the corner location count: compute point win: size of window zero_zone: window that will not consider criteria: user-specified termination criterion

  9. Optical Flow (1/9)

  10. Optical Flow (Lucas-Kanade Method) (2/9)

  11. Optical Flow (Lucas-Kanade Method) (3/9)

  12. Optical Flow (Lucas-Kanade Method) (4/9)

  13. Optical Flow (Lucas-Kanade Method) (5/9)

  14. Optical Flow (Lucas-Kanade Method) (6/9)

  15. Optical Flow(Lucas-Kanade Method) (7/9)

  16. Optical Flow(Lucas-Kanade Method) (8/9)

  17. Optical Flow(Lucas-Kanade Method) (8’/9)

  18. Optical Flow(Lucas-Kanade Method) (9/9) imgA: initial image imgB: final image pyrA, pyrB: buffers allocated to store the pyramid images featuresA: point for motion featuresB: new location point from featuresA count: number of points int the featureA winSize: window size level: depth of the stack of images status: 0/1 corresponding track_error: error value criteria: user-specified termination criterion flags: allow for some fine control

  19. Mean-Shift and Camshift Tracking (1/3) Camshift: Continuously Adaptive Mean Shift Algorithm

  20. Mean-Shift and Camshift Tracking (2/3)

  21. Mean-Shift and Camshift Tracking (3/3) prob_image: density of probable locations window: kernel window criteria: user-specified termination criterion comp: converged search window location box: contain the newly resized box

  22. Motion Templates (1/5)

  23. Motion Templates (2/5)

  24. Motion Templates (3/5)

  25. Motion Templates (4/5)

  26. Motion Templates (5/5)

  27. Estimators

  28. Estimators (The Kalman Filter)

  29. Estimators (The Kalman Filter)

  30. Estimators (The Kalman Filter)

  31. Estimators (The Kalman Filter)

  32. Estimators (The Kalman Filter)

  33. The Condensation Algorithm

  34. 감사합니다 (Q&A)

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