1. Efficient Visual Object Tracking with Online Nearest Neighbor Classifier Many slides adapt from Steve Gu

2. Application Fields Motion-based recognition ---human identification based on gait; automated surveillance --monitoring a scene to detect suspicious activities; video indexing --automatic annotation and retrieval of the videos in multimedia databases; human-computer interaction -- gesture, eye gaze tracking for data input to computers; Robot or vehicle navigation --video-based path planning and obstacle avoidance capabilities.

3. Main contributions A tracking-by-detection framework is proposed that combines nearest-neighbor classification of bags of features Efficient sub-window search A framework that handles occlusion, background clutter, scale and appearance change State-of-art results on challenging sequences Demo Demo

4. Outline Object tracking and its challenges The proposed tracking-by-detection framework –Bag-of-Features model –Online nearest neighbor classifier –Efficient sub-window search Analysis and results Result on our data

5. Challenges in object tracking Occlusion Scale change Background clutter Appearance change —loss of information from 3D world on a 2D image, —scene illumination changes —complex object motion, —nonrigid or articulated nature of objects, —real-time processing requirements.

6. Occlusion

7. Scale change

8. Background Clutter

9. Appearance change

10. Main Contributions A simple yet effective visual tracker, combine nearest-neighbor classification of bags of features A framework that handles occlusion, background clutter, scale and appearance change Can be implemented efficiently with ESS.

11. -----The main advantages of tracking by detection come from the flexibility and adaptability of its underlying representation of appearance.

12. Tracking-by-detection framework Appearance Model

13. The Objective Given: the object model in the previous frame: O k-1 the background modelB, which is static the location of the tracked window: W k Estimate the updated object model: O k

16. The Motion Model Given –the object model in the previous frame: Ok-1 –the background model B, which is static –the window in the previous frame: Wk-1 –the current test window W Compute –the matching score between Wand Wk-1given Ok-1

20. Tracking with ESS We modify the quality function: Easy to show that the quality function satisfies the criteria for branch and bound

23. Limitation SIFT descriptor cannot handle uniform regions and motion blur No advanced motion model is utilized –e.g. Kalmanfilter, particle filter, etc current tracker cannot localize objects very precisely when the object’s shape deforms.

24. Comparison with MIL

25. Application in our project Application background: Robot walks around, taking pictures intermittently; so the View, scale of object change when robot is approaching, leaving, walking around the object. As robot walking around,the background changes

26. Changes in view ( appearance ), scale, occlusion and background

27. SIFT

28. Revised Feature, from sift to color sift and dense sift Update the background model

29. Tracking result with dense-sift

30. How to improve Object representation Features representation