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Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene. Present by 陳群元. Outline. introduction Related work MAP formulation Affinity model Results Conclusion. overview. STAGE 1. STAGE 2. STAGE 3. STAGE 4. Introduction.

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Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene

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

  2. Outline • introduction • Related work • MAP formulation • Affinity model • Results • Conclusion

  3. overview

  4. STAGE 1 STAGE 2 STAGE 3 STAGE 4

  5. 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

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

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

  8. HybridBoost • combines the merits of the RankBoost algorithm and the AdaBoost algorithm .

  9. adaboost

  10. RankBoost

  11. 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 DataAssociation based Tracking algorithm • there has been no use of machine learning algorithmin building the affinity model.

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

  13. MAP formulation v1 • R = {ri} the set of all detection responses j i j i j i i j j i

  14. MAP formulation v1(cont.) • tracklet association

  15. MAP formulation v1(cont.)

  16. MAP formulation v2

  17. MAP formulation v2(cont.) • Inner cost • Transition cost

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

  19. Affinity model • Hybridboostalgorithm • Feature pool and weak learner • Training process

  20. Hybridboostalgorithm • Ie. T2 T1 T3

  21. Hybridboostalgorithm(cont.)

  22. Loss function • initial

  23. Strong ranking classifier weak weak weak weak Update sample weight Update weight Update weight

  24. Hybridboostalgorithm

  25. Feature pool and weak learner

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

  27. Training process(cont) • For each Ti ∈ T, if • connecting Ti’stail to the head of some other tracklet

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

  29. Ranking sample set

  30. Binary sample set

  31. Training process(cont.) • use thegroundtruthG and the tracklet set Tk−1 obtained from stagek − 1 to generate ranking and binary classification samples • learn a strong ranking classifier Hkby the HybridBoost algorithm • UsingHk as the affinity model to perform association on Tk−1and generate Tk

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

  33. 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

  34. Evaluation metrics

  35. 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

  36. compare

  37. 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).

  38. Strong ranking classifier output

  39. Choice of β

  40. Tracking performance

  41. 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.

  42. The end • Thank you

  43. progress

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

  45. overview

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