Method

CrossTracker [CrossTracker]


Submitted on 2 Jul. 2025 04:31 by
xin li (Beijing University of Aeronautics and Astronautics)

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

Method Description:
CrossTracker
Parameters:
CrossTracker
Latex Bibtex:

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 82.34 % 80.14 % 85.24 % 84.48 % 87.01 % 88.14 % 91.28 % 88.32 %

Benchmark TP FP FN
CAR 32585 1807 808

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 92.13 % 87.08 % 92.40 % 92 79.89 %

Benchmark MT rate PT rate ML rate FRAG
CAR 86.00 % 11.54 % 2.46 % 133

Benchmark # Dets # Tracks
CAR 33393 715

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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