Method

SST [st] [SST [st]]
[Anonymous Submission]

Submitted on 29 Dec. 2023 07:25 by
[Anonymous Submission]

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

Method Description:
we propose a 3D multi-object tracking system based
on stereo cameras.
Parameters:
\alpha=0.4
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 59.90 % 57.19 % 63.28 % 60.64 % 82.13 % 66.26 % 87.39 % 85.65 %
PEDESTRIAN 28.65 % 19.93 % 41.29 % 22.33 % 50.36 % 46.22 % 58.52 % 71.23 %

Benchmark TP FP FN
CAR 24281 10111 1114
PEDESTRIAN 7205 15945 3059

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 66.95 % 83.86 % 67.36 % 143 55.55 %
PEDESTRIAN 17.30 % 64.74 % 17.91 % 140 6.33 %

Benchmark MT rate PT rate ML rate FRAG
CAR 43.69 % 36.15 % 20.15 % 207
PEDESTRIAN 9.97 % 23.02 % 67.01 % 700

Benchmark # Dets # Tracks
CAR 25395 805
PEDESTRIAN 10264 388

This table as LaTeX

This figure as: png pdf

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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