Method

LG-FusionTrack [on] [LG-FusionTrack [on]]


Submitted on 7 Jul. 2025 15:40 by
Xiangyan Yan (Chongqing University SLAMMOT Team)

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

Method Description:
N/A
Parameters:
N/A
Latex Bibtex:
N/A

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 90.78 % 87.49 % 90.86 % 89.95 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.48 % 98.44 % 95.90 % 36714 583 2559 5.24 % 41407 957

Benchmark MT PT ML IDS FRAG
CAR 82.00 % 15.69 % 2.31 % 30 279

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.


eXTReMe Tracker