Method

Multi-modal 3D Multi-object Tracking with Robust Association and Track Drift Compensation [RA3DMOT]
[Anonymous Submission]

Submitted on 20 Mar. 2024 22:23 by
[Anonymous Submission]

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

Method Description:
a 3D MOT framework with robust association and
track drift compensation
Parameters:
nan
Latex Bibtex:
nan

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 79.36 % 75.34 % 84.23 % 83.53 % 82.31 % 87.28 % 91.39 % 88.35 %

Benchmark TP FP FN
CAR 32188 2204 2712

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 85.53 % 87.22 % 85.71 % 61 73.56 %

Benchmark MT rate PT rate ML rate FRAG
CAR 83.39 % 14.77 % 1.85 % 625

Benchmark # Dets # Tracks
CAR 34900 1070

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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