GRAP-MOT: Unsupervised Graph-based Position Weighted Person Multi-camera Multi-object Tracking in a Highly Congested Space

TL;DR

GRAP-MOT is an unsupervised, graph-based multi-camera person tracking method designed for highly congested, closed-area environments, integrating position estimation to improve identification accuracy.

Contribution

It introduces a novel graph-weighted approach with online label updating and position estimation, enhancing multi-object tracking in crowded spaces.

Findings

Outperforms existing methods on real and simulated datasets.

Position estimation improves tracking accuracy.

IDF1 metric is more suitable than MOTA for crowded environments.

Abstract

GRAP-MOT is a new approach for solving the person MOT problem dedicated to videos of closed areas with overlapping multi-camera views, where person occlusion frequently occurs. Our novel graph-weighted solution updates a person's identification label online based on tracks and the person's characteristic features. To find the best solution, we deeply investigated all elements of the MOT process, including feature extraction, tracking, and community search. Furthermore, GRAP-MOT is equipped with a person's position estimation module, which gives additional key information to the MOT method, ensuring better results than methods without position data. We tested GRAP-MOT on recordings acquired in a closed-area model and on publicly available real datasets that fulfil the requirement of a highly congested space, showing the superiority of our proposition. Finally, we analyzed existing metrics used to compare MOT algorithms and concluded that IDF1 is more adequate than MOTA in such comparisons. We made our code, along with the acquired dataset, publicly available.