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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 85.56 % 87.19 % 85.72 % 89.85 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.43 % 93.24 % 93.83 % 37340 2709 2201 24.35 % 47148 1350

Benchmark MT PT ML IDS FRAG
CAR 83.38 % 14.77 % 1.85 % 57 622

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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