Method

MTracker [MTracker]


Submitted on 17 Jul. 2024 16:47 by
Xiyang Wang (MCDrive)

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

Method Description:
TBD
Parameters:
TBD
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 82.29 % 79.00 % 86.33 % 82.42 % 87.54 % 89.30 % 91.31 % 88.05 %

Benchmark TP FP FN
CAR 31899 2493 481

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.31 % 86.83 % 91.35 % 15 79.09 %

Benchmark MT rate PT rate ML rate FRAG
CAR 85.54 % 6.31 % 8.15 % 71

Benchmark # Dets # Tracks
CAR 32380 634

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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