Method

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


Submitted on 13 Feb. 2023 02:20 by
Phu Ninh (Msis Lab, Chungbuk National University)

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

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=CenterTrack
\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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 89.60 % 85.04 % 89.96 % 87.80 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.37 % 97.73 % 95.50 % 36630 851 2603 7.65 % 45566 1296

Benchmark MT PT ML IDS FRAG
CAR 82.31 % 15.38 % 2.31 % 123 331

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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