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

Submitted on 18 Jan. 2023 07:43 by
[Anonymous Submission]

Running time:0.05 s
Environment:GPU @ 2.5 Ghz (Python)

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 (CollabMOT)
method which performs online association of
multiple tracked vehicles from stereo vision
camera. CollabMOT 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

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 73.39 % 74.85 % 72.58 % 80.19 % 83.11 % 75.56 % 87.93 % 86.15 %

Benchmark TP FP FN
CAR 31839 2553 1342

CAR 87.92 % 84.50 % 88.67 % 261 73.57 %

Benchmark MT rate PT rate ML rate FRAG
CAR 84.61 % 13.08 % 2.31 % 228

Benchmark # Dets # Tracks
CAR 33181 1032

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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