Method

FusionTrack+pointgnn [FusionTrack+pointgnn]


Submitted on 19 Jan. 2024 07:37 by
weizhen zeng (Tongji university)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
3D detection is pointgnn, 2D detection is rrc.
Parameters:
GIOU threshold is -0.2 as AB3DMOT
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 89.67 % 85.57 % 89.74 % 88.49 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.95 % 99.55 % 95.06 % 33934 152 3376 1.37 % 37665 782

Benchmark MT PT ML IDS FRAG
CAR 76.77 % 19.38 % 3.85 % 26 316

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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