Detection by Detections: Non-parametric Detector Adaptation for a Video

1. Detection by Detections: Non-parametric Detector Adaptation for a Video

2. Outline • Introduction • Non-parametric detector adaption • Binary codes with a vocabulary tree • Similarity measure of the binary codes • Transfer classification • Identity grouping of detections • Experiment • Conclusion

3. Introduction

4. Introduction Extend any static-image-based object detector to video object detection. Needs neither the original training data, nor manually labeled online examples.

5. Non-parametric detector adaption 1.Set generic human detector work on high recall low precision point 2.To build a vocabulary tree using hierarchical k-means[19] 3.Encode the set of most confident visual detections as a set of binary vectors [19] D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In CVPR, 2006.

6. Binary codes with a vocabulary tree

7. Binary codes with a vocabulary tree Define a mapping T

8. Similarity measure of the binary codes ||c||: the number of non-zero bits in the binary vector c

9. Transfer classification compose positive pool change representation similarity scoring final classification decision

10. Identity grouping of detections Group ID g(u) of any other example u

11. Experiment The normalization scheme evaluation

12. Experiment The evaluation on the threshold for positive pool

13. Experiment The tree depth exploration

14. Experiment Performance of video object detection Data is from the EC Funded CAVIAR project/IST 2001 37540 http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

15. Experiment

16. Experiment Present the adaption performance based on [6] [6] P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, deformable part model. In CVPR, 2008.

17. Experiment

18. Experiment Identity grouping F(mode(gi)) counts the number of examples in group i with identity mode(gi)

19. Experiment On positive pool On all detection

20. Experiment Difficult to be detected objects in video

21. Experiment First row by our approach Second row by k-means clustering

22. Conclusion Simple and effective solution to improve the pure detection accuracy of off-shelf detectors trained from static images on target videos.