Method

Laser_Points_image_GPS_Online_PMTrack [PMTrack]
[Anonymous Submission]

Submitted on 8 Apr. 2024 03:32 by
[Anonymous Submission]

Running time:0.02 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
A 3D multi-target tracking based on parallel multimodal data association. It'a a TBD.
The detector uses virconv
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 81.36 % 78.90 % 84.49 % 82.98 % 86.76 % 87.73 % 90.18 % 88.02 %

Benchmark TP FP FN
CAR 32130 2262 764

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 91.13 % 86.79 % 91.20 % 25 78.78 %

Benchmark MT rate PT rate ML rate FRAG
CAR 87.23 % 6.15 % 6.62 % 82

Benchmark # Dets # Tracks
CAR 32894 650

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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