Method

DetNosNa [DFR]
[Anonymous Submission]

Submitted on 21 May. 2024 11:04 by
[Anonymous Submission]

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

Method Description:
Anonymous
Parameters:
Anonymous
Latex Bibtex:
Anonymous

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 81.63 % 78.59 % 85.39 % 84.16 % 84.98 % 89.11 % 90.15 % 87.81 %

Benchmark TP FP FN
CAR 32673 1719 1388

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.94 % 86.54 % 90.97 % 9 78.16 %

Benchmark MT rate PT rate ML rate FRAG
CAR 83.69 % 6.31 % 10.00 % 70

Benchmark # Dets # Tracks
CAR 34061 618

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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