Method

CollabMOT Stereo Camera Collaborative Multi Object Tracking [st] [CollabMOT ]


Submitted on 19 Jan. 2024 02:25 by
Phu Ninh (Msis Lab, Chungbuk National University)

Running time:0.02 s
Environment:4 cores @ 2.5 Ghz (Python)

Method Description:
Most 2D MOT algorithms primarily utilize only
single-camera view. Therefore, they are prone to
frequent tracking losses and track-ID switching
under conditions due to limited viewpoints and
occluded objects. To alleviate this problem, we
propose a stereo-camera-based collaborated multi-
object tracking (CollabMOT) method that performs
online and dynamic association of multiple
tracklets from baseline MOT algorithms in
overlapping views of stereo cameras. CollabMOT
utilizes appearance similarity to generate global
tracking IDs that unify the same tracklets between
viewpoints of stereo cameras. It then leverages
the transitive information from these global
tracking IDs to reconnect the disrupted tracklets
in each camera view. CollabMOT improves the
overall performance of baseline 2D MOT methods on
a single camera view by mitigating the problem of
ID switching.
Parameters:
\left=RAM
\right=DeepSORT
Latex Bibtex:
@ARTICLE{10410636,
author={Ninh, Phu and Kim, Hyungwon},
journal={IEEE Access},
title={CollabMOT Stereo Camera Collaborative
Multi Object Tracking},
year={2024},
volume={},
number={},
pages={1-1},
doi={10.1109/ACCESS.2024.3356864}}

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

Benchmark MOTA MOTP MODA IDSW sMOTA
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.


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