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

TL;DR

This paper introduces Deep-EIoU, a novel online multi-object tracking method tailored for sports scenarios, which uses iterative scale-up ExpansionIoU and deep features to effectively track irregular motion objects without relying on the Kalman filter.

Contribution

The paper proposes Deep-EIoU, a new online tracking approach that abandons the Kalman filter and leverages iterative ExpansionIoU with deep features for improved sports object tracking.

Findings

Achieves 77.2% HOTA on SportsMOT dataset

Achieves 85.4% HOTA on SoccerNet-Tracking dataset

Outperforms previous state-of-the-art trackers in sports scenarios

Abstract

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.