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 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 81.78 % 79.15 % 85.15 % 82.78 % 87.82 % 87.63 % 92.15 % 88.54 %

Benchmark TP FP FN
CAR 31831 2561 585

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.70 % 87.44 % 90.85 % 51 79.08 %

Benchmark MT rate PT rate ML rate FRAG
CAR 81.85 % 15.69 % 2.46 % 288

Benchmark # Dets # Tracks
CAR 32416 738

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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