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 HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 82.79 % 79.49 % 86.84 % 82.86 % 88.14 % 89.47 % 92.03 % 88.49 %

Benchmark TP FP FN
CAR 31885 2507 447

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.38 % 87.38 % 91.41 % 10 79.68 %

Benchmark MT rate PT rate ML rate FRAG
CAR 83.54 % 10.00 % 6.46 % 97

Benchmark # Dets # Tracks
CAR 32332 645

This table as LaTeX

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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