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

Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene. Yuan Li, Chang Huang and Ram Nevatia. Yuan Li. outline. Instruction Related work MAP formulation Affinity model Results Conclusion. overview. 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 Yuan Li, Chang Huang and Ram Nevatia

  2. Yuan Li

  3. outline • Instruction • Related work • MAP formulation • Affinity model • Results • Conclusion

  4. overview

  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 • http://www.cmlab.csie.ntu.edu.tw/~cyy/learning/tutorials/AdaBoostBinary.pdf

  10. RankBoost • learning to rank as a problem of binary classification on instance pairs • trains one weak ranker at each round of iteration • //re-weighted: it decreases the weight of correctly ranked pairs and increases the weight of wrongly ranked pairs.??

  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

  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.

  21. Hybridboostalgorithm(cont.)

  22. Loos function • initial

  23. Hybridboostalgorithm

  24. Feature pool and weak learner

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

  26. Training process(cont) • For each Ti ∈ T, if • connecting Ti’stail to the head of some other tracklet • connecting Ti’s head to the tail of some other tracklet before Ti which is also matched to G

  27. Ranking sample set

  28. Binary sample set

  29. 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

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

  31. 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

  32. Evaluation metrics

  33. 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

  34. compare

  35. 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).

  36. Strong ranking classifier output

  37. Choice of β

  38. Tracking performance

  39. Conclusion and future work • Use HybridBoostalgorithm 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.

  40. problem • tracklet ?affinity model?圓圈?路徑? • automatically selectamong various features andcorresponding non-parametric models? Rankboost ? Adaboost? 匈牙利演算法

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