1 research outputs found
Joint Object Detection and Multi-Object Tracking with Graph Neural Networks
Object detection and data association are critical components in multi-object
tracking (MOT) systems. Despite the fact that the two components are dependent
on each other, prior works often design detection and data association modules
separately which are trained with separate objectives. As a result, one cannot
back-propagate the gradients and optimize the entire MOT system, which leads to
sub-optimal performance. To address this issue, recent works simultaneously
optimize detection and data association modules under a joint MOT framework,
which has shown improved performance in both modules. In this work, we propose
a new instance of joint MOT approach based on Graph Neural Networks (GNNs). The
key idea is that GNNs can model relations between variable-sized objects in
both the spatial and temporal domains, which is essential for learning
discriminative features for detection and data association. Through extensive
experiments on the MOT15/16/17/20 datasets, we demonstrate the effectiveness of
our GNN-based joint MOT approach and show state-of-the-art performance for both
detection and MOT tasks. Our code is available at:
https://github.com/yongxinw/GSDTComment: Published in International Conference on Robotics and Automation
(ICRA), 2021. Code is released here: https://github.com/yongxinw/GSD