Method

Anonymous [la] [Anonymous]
[Anonymous Submission]

Submitted on 27 Jun. 2026 08:59 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
This work is being prepared for submission to a
peer-reviewed conference, and detailed methodology
will be provided after the review process.
Parameters:
input_score_3d=0.30

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 83.62 % 80.30 % 87.68 % 83.38 % 88.73 % 90.22 % 92.43 % 88.70 %

Benchmark TP FP FN
CAR 31974 2418 345

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.93 % 87.61 % 91.97 % 13 80.41 %

Benchmark MT rate PT rate ML rate FRAG
CAR 84.77 % 7.23 % 8.00 % 53

Benchmark # Dets # Tracks
CAR 32319 623

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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