Robust Object Tracking Using Local Motion Histogram Model (LMHM)

1. Robust Object Tracking Using Local Motion Histogram Model (LMHM) Hyungtae Lee University of Maryland htlee@umd.edu UKC2013 New York/New Jersey

2. UKC2013 New York/New Jersey Contexts Contexts MOTIVATION PROPOSED APPROACH EXPERIMENTS CONCLUSION Local Motion Histogram Model

3. UKC2013 New York/New Jersey Motivation

4. UKC2013 New York/New Jersey Motivation Motivation Detection-based Tracking Method Frame by Frame Updating-based Tracking Method Do not require detection Handle deformable objects

5. UKC2013 New York/New Jersey Motivation Motivation Conservatively maintaining an appearance model It may fail due to accumulated errors from local deformation Aggressively updating the model It may fail due to its inability to separate the foreground object from the background

6. UKC2013 New York/New Jersey Motivation Motivation Image motion to distinguish an object from background Any image element (feature or pixel) would be the part of the motion if it is close to the motion and moves consistently with the object Probabilistic Occupancy Map (POM) (a) (b) (c) (d) (e) Example of the POM

7. UKC2013 New York/New Jersey Proposed Approach

8. UKC2013 New York/New Jersey Proposed Approach Local Motion Searching and Clustering by the Weighted Mean Shift Local Motion Searching MSER + SIFT descriptor Reject outliers by RANSAC (Random Sample Consensus) Clustering by the Weighted Mean Shift Prevent from converging to an ineffective motion Emphasize the stability of local motion

9. UKC2013 New York/New Jersey Proposed Approach Density function of object motion After RANSAC LMHM Local Motion Histogram Model (LMHM) (a) (b) (c) (d) Local Motion Histogram Model (LMHM) Clustered mode ( ) and their stabilities ( ) Stability

10. UKC2013 New York/New Jersey Proposed Approach Probabilistic Occupancy Map (POM) Probabilistic Occupancy Map (POM) Generate the motion histogram for every pixel The probability that pixel is occupied by the object given its candidate corresponding pixel is also occupied by the object Likelihood

11. UKC2013 New York/New Jersey Proposed Approach Probabilistic Occupancy Map (POM) . . . Probabilistic Occupancy Map (POM) : the set of the candidate pixels for correspondence of LMHM

12. UKC2013 New York/New Jersey Proposed Approach Probabilistic Occupancy Map (POM) Probabilistic Occupancy Map (POM) Probability that is occupied by at least one of the points Probability that given

13. UKC2013 New York/New Jersey Proposed Approach Probabilistic Occupancy Map (POM) Probabilistic Occupancy Map (POM) , where

14. UKC2013 New York/New Jersey Experiments

15. UKC2013 New York/New Jersey Experiments Experiments Setting Common challenge Dramatic variant of object appearance: car sequence Illumination variation: corridor sequence Occluded target in a cluttered background: subway sequence Additional interest task Recognizing new object: group sequence

16. UKC2013 New York/New Jersey Experiments Experiments SIFT tracker Tran’s tracker Proposed tracker Common challenge car sequence

17. UKC2013 New York/New Jersey Experiments Experiments SIFT tracker Tran’s tracker Proposed tracker Common challenge corridor sequence

18. UKC2013 New York/New Jersey Experiments Experiments SIFT tracker Tran’s tracker Proposed tracker Common challenge subway sequence

19. UKC2013 New York/New Jersey Experiments Experiments Common challenge Mean distance between centers Mean PASCAL score

20. UKC2013 New York/New Jersey Experiments Experiments Recognizing New Object group sequence

21. UKC2013 New York/New Jersey Conclusion

22. UKC2013 New York/New Jersey Experiments Conclusion The LMHM reduces the complexity computing motion distribution by replacing the domain of distribution to the set of motion candidates. Generating the motion distribution for every pixel overcomes the deficiency of other tracker using motion information which tends to include background elements into the foreground object model. For every pixel, we evaluate the probability that the pixel is occupied by foreground and background.