Method

Redefining Similarity Measurement in 3D Multi-Object Tracking [SSL3DMOT]
[Anonymous Submission]

Submitted on 21 Aug. 2023 15:05 by
[Anonymous Submission]

Running time:3 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
In this paper, we propose a novel approach called
CM-MCL3DMOT to calculate object feature similarity
by employing self-supervised cross-modal momentum
contrastive learning. We introduce three key
techniques.
Parameters:
0.2
Latex Bibtex:

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 87.36 % 87.64 % 87.46 % 90.18 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.77 % 98.84 % 94.09 % 34337 402 3911 3.61 % 38213 749

Benchmark MT PT ML IDS FRAG
CAR 74.31 % 19.38 % 6.31 % 33 343

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.


eXTReMe Tracker