Method

Redefining Similarity Measurement in 3D Multi-Object Tracking [CMSSL3DMOT]
[Anonymous Submission]

Submitted on 4 Dec. 2023 12:55 by
[Anonymous Submission]

Running time:268 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
we propose a novel approach called CMSSL3DMOT to
calculate object feature similarity by employing
cross-modal contrastive self-supervised learning. We
introduce three key techniques.
Parameters:
0.75
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 79.61 % 76.32 % 83.64 % 79.20 % 88.53 % 86.14 % 91.93 % 88.65 %

Benchmark TP FP FN
CAR 30406 3986 363

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 87.24 % 87.56 % 87.36 % 38 76.25 %

Benchmark MT rate PT rate ML rate FRAG
CAR 72.92 % 20.46 % 6.62 % 350

Benchmark # Dets # Tracks
CAR 30769 642

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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