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 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 79.64 % 76.36 % 83.68 % 79.36 % 88.38 % 86.26 % 91.86 % 88.62 %

Benchmark TP FP FN
CAR 30479 3913 404

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 87.29 % 87.55 % 87.45 % 53 76.26 %

Benchmark MT rate PT rate ML rate FRAG
CAR 74.00 % 19.69 % 6.31 % 346

Benchmark # Dets # Tracks
CAR 30883 649

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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