Method

MCT[la][on] [MCT]


Submitted on 24 Jul. 2018 04:31 by
dawei zhao (National University of Defense Technology)

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

Method Description:
TBA
Parameters:
TBA
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 83.86 % 85.87 % 84.20 % 88.87 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 86.33 % 99.14 % 92.30 % 32552 281 5154 2.53 % 36078 1301

Benchmark MT PT ML IDS FRAG
CAR 61.69 % 28.77 % 9.54 % 115 507

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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