Multi-Camera Collaborative Multi Object Tracking-CenterTrack_DeepSort [st] [MCMOT CenterTrack]
[Anonymous Submission]

Submitted on 13 Feb. 2023 02:20 by
[Anonymous Submission]

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
Most of multi-object tracking methods based on

deep learning, however, are highly prone to

frequent tracking losses and track-ID switching in

case of limited viewpoint and occluded objects. To

alleviate this problem, we propose a multi-camera

Collaborate Multi Object Tracking (CMOT) method

which performs online association of multiple

tracked vehicles from stereo vision camera. CMOT

not only provides global tracking IDs between

multiple cameras but also helps reduce the problem

of ID switching, tracklet missing and false

positive compared with the conventional multi-

object tracking based on single camera

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 75.26 % 75.46 % 75.74 % 80.06 % 84.36 % 78.75 % 88.53 % 86.44 %

Benchmark TP FP FN
CAR 31752 2640 888

CAR 89.08 % 84.97 % 89.74 % 227 75.20 %

Benchmark MT rate PT rate ML rate FRAG
CAR 82.15 % 15.38 % 2.46 % 220

Benchmark # Dets # Tracks
CAR 32640 922

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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