Iterative Scale-Up ExpansionIoU and Deep Features Association for Multi-Object Tracking in Sports

• Main Authors: Huang, Hsiang-Wei, Yang, Cheng-Yen, Sun, Jiacheng, Kim, Pyong-Kun, Kim, Kwang-Ju, Lee, Kyoungoh, Huang, Chung-I, Hwang, Jenq-Neng
• Format: Journal Article
• Language: English
• Published: 22-06-2023
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2306.13074

Deep learning-based object detectors have driven notable progress in
multi-object tracking algorithms. Yet, current tracking methods mainly focus on
simple, regular motion patterns in pedestrians or vehicles. This leaves a gap
in tracking algorithms for targets with nonlinear, irregular motion, like
athletes. Additionally, relying on the Kalman filter in recent tracking
algorithms falls short when object motion defies its linear assumption. To
overcome these issues, we propose a novel online and robust multi-object
tracking approach named deep ExpansionIoU (Deep-EIoU), which focuses on
multi-object tracking for sports scenarios. Unlike conventional methods, we
abandon the use of the Kalman filter and leverage the iterative scale-up
ExpansionIoU and deep features for robust tracking in sports scenarios. This
approach achieves superior tracking performance without adopting a more robust
detector, all while keeping the tracking process in an online fashion. Our
proposed method demonstrates remarkable effectiveness in tracking irregular
motion objects, achieving a score of 77.2% HOTA on the SportsMOT dataset and
85.4% HOTA on the SoccerNet-Tracking dataset. It outperforms all previous
state-of-the-art trackers on various large-scale multi-object tracking
benchmarks, covering various kinds of sports scenarios. The code and models are
available at https://github.com/hsiangwei0903/Deep-EIoU.