Multi-Camera Collaborative Multi Object Tracking-RAM-DeepSort [st] [CollabMOT-RAM]
[Anonymous Submission]

Submitted on 17 Jan. 2023 14:36 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)
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 80.02 % 78.85 % 81.86 % 82.60 % 86.32 % 85.34 % 88.41 % 87.14 %

Benchmark TP FP FN
CAR 32329 2063 583

CAR 91.70 % 85.77 % 92.31 % 207 78.33 %

Benchmark MT rate PT rate ML rate FRAG
CAR 86.31 % 11.23 % 2.46 % 156

Benchmark # Dets # Tracks
CAR 32912 708

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.

eXTReMe Tracker