1. Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene Present by 陳群元

2. Yuan Li

3. Outline introduction Related work MAP formulation Affinity model Results Conclusion

6. overview

8. Introduction learning-based hierarchical approach of multi-target tracking HybridBoost algorithm-hybrid loss function association of tracklet is formulated as a joint problem of ranking and classification

9. ranking the ranking part aims to rank correct tracklet associations higher than other alternatives

10. classification the classification part is responsible to reject wrong associations when no further association should be done

11. HybridBoost combines the merits of the RankBoost algorithm and the AdaBoost algorithm.

12. adaboost

13. RankBoost

14. Related work the earliest works look at a longer period of time in contrast to frame-by-frame tracking. To overcome this, a category of Data Association based Tracking algorithm there has been no use of machine learning algorithm in building the affinity model.

15. MAP formulation Robust Object Tracking by Hierarchical Association of Detection Responses ours

16. MAP formulation v1 R = {ri} the set of all detection responses jj jjj ii ii i

17. MAP formulation v1(cont.) tracklet association

18. MAP formulation v1(cont.)

19. MAP formulation v2

20. MAP formulation v2(cont.) Inner cost Transition cost

21. MAP formulation v2(cont.) With these,we can rewrite it

22. Affinity model Hybridboost algorithm Feature pool and weak learner Training process

23. Hybridboost algorithm Ie. T1 T2 T3

24. Hybridboost algorithm(cont.)

25. Loss function initial

27. Hybridboost algorithm

28. Weak ranking classifier Feature & threshold

30. Feature pool and weak learner

31. Training process T:tracklet set from the previous stage G:groundtruth track set

32. Training process (cont) For each Ti ∈ T, if connecting Ti’s tail to the head of some other tracklet

33. Training process (cont) connecting Ti’s head to the tail of some other tracklet before Ti which is also matched to G

34. Ranking sample set

35. Binary sample set

36. Training process (cont.) use the groundtruth G and the tracklet set Tk−1 obtained from stage k − 1 to generate ranking and binary classification samples learn a strong ranking classifier Hk by the HybridBoost algorithm Using Hk as the affinity model to perform association on Tk−1 and generate Tk

37. Experimental results Implementation details Evaluation metrics Analysis of the training process Tracking performance

38. Implementation details dual-threshold strategy to generate short but reliable tracklets four stages of association maximum allowed frame gap 16, 32, 64 and 128 a strong ranking classifier H with 100 weak ranking classifiers Β=0.75 ζ = 0

39. Evaluation metrics

40. track fragments &ID switches Traditional ID switch:“two tracks exchanging their ids”. ID switch : a tracked trajectory changing its matched GT ID track fragments:more strict

41. compare

42. Best features Motion smoothness (feature type 13 or 14) color histogram similarity (feature 4) number of miss detected frames in the gap between the two trackelts (feature 7 or 9).

43. Strong ranking classifier output

44. Choice of β

45. Tracking performance

46. Conclusion and future work Use HybridBoost algorithm to learn the affinity model as a joint problem of ranking and classification The affinity model is integrated in a hierarchical data association framework to track multiple targets in very crowded scenes.

48. The end –Thank you

49. System Architecture 完成度項目 100% Ground truth data (CAVIAR 、 TRECUID08) 50%User Interface for ground truth 50% Ground truth Learning phase 1 、 2 、 3 、 4 30%Feature Extraction 0%Dual threshold method 0% Input data training phase 1 、 2 、 3 、 4

50. Another paper Tracking with Local Spatio- Temporal Motion Patterns in Extremely Crowded Scenes

52. problem tracklet ?affinity model? 圓圈 ? 路徑 ? automatically select among various features and corresponding non-parametric models? Rankboost ? Adaboost? 匈牙利演算法