Method

FusionTrack+pointgnn [FusionTrack+pointgnn]


Submitted on 19 Jan. 2024 07:37 by
weizhen zeng (Tongji university)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
3D detection is pointgnn, 2D detection is rrc.
Parameters:
GIOU threshold is -0.2 as AB3DMOT
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 78.50 % 75.96 % 81.89 % 78.86 % 87.01 % 84.40 % 90.83 % 87.02 %

Benchmark TP FP FN
CAR 31009 3383 159

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 89.57 % 85.51 % 89.70 % 46 76.51 %

Benchmark MT rate PT rate ML rate FRAG
CAR 76.31 % 19.85 % 3.85 % 302

Benchmark # Dets # Tracks
CAR 31168 668

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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