Method

CrossTracker [CrossTracker]


Submitted on 5 Sep. 2023 15:42 by
wenqi lu (Nanjing University of Aeronautics and Astronautics)

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
a multi-modal MOT method
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 82.00 % 79.96 % 84.75 % 83.48 % 87.92 % 87.20 % 92.12 % 88.39 %

Benchmark TP FP FN
CAR 32178 2214 480

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.97 % 87.19 % 92.17 % 68 79.98 %

Benchmark MT rate PT rate ML rate FRAG
CAR 85.08 % 12.31 % 2.62 % 180

Benchmark # Dets # Tracks
CAR 32658 718

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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