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E fficient Visual Object T racking with O nline N earest N eighbor Classifier

This paper proposes a tracking-by-detection framework for efficient visual object tracking. It combines nearest-neighbor classification of bags of features and efficient sub-window search. The framework can handle challenges such as occlusion, background clutter, scale change, and appearance change. State-of-the-art results are achieved on challenging sequences.

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E fficient Visual Object T racking with O nline N earest N eighbor Classifier

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  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

  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: Ok-1 • the background modelB, which is static • the location of the tracked window: Wk Estimate • the updated object model: Ok

  14. 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

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

  16. 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.

  17. Comparison with MIL

  18. 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

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

  20. SIFT

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

  22. Tracking result with dense-sift

  23. How to improve • Object representation • Features representation

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