Method

ET-MOT[on] [ET-MOT]


Submitted on 22 Sep. 2017 15:14 by
Qiang Fang (cainiao)

Running time:0.7 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
private
Parameters:
detction confidence threshold = 0.3
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
PEDESTRIAN 51.44 % 72.65 % 53.15 % 91.20 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 64.36 % 85.61 % 73.48 % 15027 2526 8320 22.71 % 21375 306

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 25.43 % 56.36 % 18.21 % 396 1405

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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