The KITTI Vision Benchmark Suite

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.

\left=RAM
\right=DeepSORT

@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}}