Method

Smart Multiple Affinity Tracking [on] [SMAT]


Submitted on 28 Jun. 2019 10:05 by
Nicolas Franco Gonzalez (Grenoble INP)

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

Method Description:
Tracking using optical flow, intersection over union and
pose estimation. Hungarian method association.
Parameters:
score>0.5 accepted
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 74.35 % 84.75 % 75.66 % 88.20 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 79.18 % 98.35 % 87.73 % 29925 503 7868 4.52 % 32964 2239

Benchmark MT PT ML IDS FRAG
CAR 56.77 % 37.23 % 6.00 % 452 1313

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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