Method

Co-MOT [Co-MOT]


Submitted on 4 Jan. 2025 13:18 by
Xingdi Liu (Nanjing University of Posts and Telecommunications)

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

Method Description:
In this paper, we propose a GNN based 3D MOT method
which effectively utilizes the collective motion
consistency in traffic flow.
Parameters:
Please check the paper.
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 75.76 % 74.01 % 78.13 % 80.89 % 82.40 % 80.47 % 90.35 % 87.11 %
PEDESTRIAN 43.52 % 39.88 % 47.97 % 44.51 % 58.08 % 52.00 % 64.83 % 71.35 %

Benchmark TP FP FN
CAR 31756 2636 2004
PEDESTRIAN 14501 8649 3238

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.29 % 85.62 % 86.51 % 420 72.01 %
PEDESTRIAN 47.65 % 64.61 % 48.65 % 232 25.48 %

Benchmark MT rate PT rate ML rate FRAG
CAR 81.54 % 16.15 % 2.31 % 599
PEDESTRIAN 29.55 % 51.20 % 19.24 % 1535

Benchmark # Dets # Tracks
CAR 33760 1326
PEDESTRIAN 17739 882

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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