Method

Smart Multiple Affinity Tracking [on] [SMAT]


Submitted on 17 Jul. 2019 14:22 by
Nicolas Franco Gonzalez (Grenoble INP)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Tracking using optical flow, appearance vector,
intersection over union and pose estimation. Hungarian
method association
Parameters:
Score>0.5
Latex Bibtex:

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 78.93 % 84.29 % 79.39 % 87.82 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 83.31 % 97.71 % 89.94 % 31675 741 6346 6.66 % 35688 1445

Benchmark MT PT ML IDS FRAG
CAR 63.85 % 31.38 % 4.77 % 160 679

This table as LaTeX


[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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