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Real-time foreground object detection & tracking with moving camera

Real-time foreground object detection & tracking with moving camera. P93922005 Martin Chang. Motivation. More and more moving cameras Handheld devices Cell phone PDA Hard to track object with moving camera Hard to learn background with moving camera. Previous Work.

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Real-time foreground object detection & tracking with moving camera

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  1. Real-time foreground object detection & tracking with moving camera P93922005 Martin Chang

  2. Motivation • More and more moving cameras • Handheld devices • Cell phone • PDA • Hard to track object with moving camera • Hard to learn background with moving camera

  3. Previous Work • Thompson, W.B. and Pong, T.C. Detecting moving objects. International Journal of Computer Vision, 4(1):39-57. (January 1990). • Stationary Camera • K Daniilidis, C Krauss, M Hansen, G Sommer. Real-Time Tracking of Moving Objects with an Active Camera. Real-Time Imaging, 1998. • Two degrees of freedom of a camera platform • E Hayman, JO Eklundh - Procs. Statistical Background Subtraction for a Mobile Observer. IEEE Intl. Conf. on Computer Vision. • Moving foreground object • static background • Mobile observer

  4. Steps • Find good feature to track • Track features • Classify foreground and background features • Decide region of foreground object • Track foreground object

  5. Video Demo

  6. Step 1: Find good feature to track • Finding good feature to track • Shi and Tomasi ‘s method

  7. Step 2: Track features • Optical flow

  8. Step 3: Classify foreground and background features • Classify feature points • Optical flow • Moving direction of feature • Length of moving direction • MTF of neighbor image patch • Doesn’t work, due to • With cheap camera • Low resolution video

  9. Idea: how to identify foreground features? 1/3 • Case 1: The camera rotates • The background image moves more Background Object

  10. Idea: how to identify foreground features? 2/3 • Case 2: The background moves • The background image moves more Background object

  11. Idea: how to identify foreground features? 3/3 • Case 3: The object moves • The foreground image moves more Background object

  12. Classify Features • KMeans • Hard to separate them well • Marginal KMeans • Filter unreliable features • Angle issue • 1° is similar to 359 °

  13. KMeans

  14. Marginal KMeans (Margin=1/2)

  15. Marginal KMeans (Margin=1/4)

  16. Step 4: Foreground Object Detection 1/2 • Two two-class problems • Classify foreground and background features • Cluster features • Calculate the occlusion rate • The region of foreground object should be • Compact • Less noise (background features)

  17. Foreground Object Detection 2/2 • Measure our confidence • Geometry approach • Check foreground and background regions

  18. Step 5: Foreground Object Tracking • Object detection • If foreground object is never detected Go to Step 1 • Object tracking • Go to Step 1

  19. Development Platform • Microsoft Visual C++ .NET 2003 • Cheap webcam (USB 1.1) • OpenCV

  20. Future Work • Find parameters by machine learning • Detect finite candidate objects • Cue: color moment • Multiple object detection(!)

  21. Conclusion • The bottleneck is camera’s data transporting speed (USB 1.1) • Real time is possible • OpenCV is useful

  22. Demo

  23. Thanks!

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