Method

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 (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
Parameters:
\sct_method=centertrack
\flip_test=False
\maximum_overlapped=0.5

\frame_offset=0
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

Benchmark MOTA MOTP MODA IDSW sMOTA
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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