Method

LG-FusionTrack [LG-FusionTrack]


Submitted on 22 Apr. 2025 07:57 by
Xiangyan Yan (Chongqing University SLAMMOT Team)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
N/A
Parameters:
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Latex Bibtex:
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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 91.36 % 87.40 % 91.43 % 89.91 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.63 % 98.81 % 96.15 % 36822 444 2504 3.99 % 40937 701

Benchmark MT PT ML IDS FRAG
CAR 83.54 % 10.00 % 6.46 % 25 102

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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