Method

FullMOT [FullMOT]
[Anonymous Submission]

Submitted on 20 Feb. 2025 04:47 by
[Anonymous Submission]

Running time:0.1s
Environment:8 cores @ 2.5 Ghz (Python)

Method Description:
None
Parameters:
None
Latex Bibtex:
None

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 81.10 % 78.20 % 84.75 % 83.21 % 85.69 % 87.47 % 91.51 % 88.09 %

Benchmark TP FP FN
CAR 32192 2200 1206

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.01 % 86.84 % 90.10 % 30 77.69 %

Benchmark MT rate PT rate ML rate FRAG
CAR 82.77 % 15.08 % 2.15 % 387

Benchmark # Dets # Tracks
CAR 33398 771

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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