Basketball-SORT: An Association Method for Complex Multi-object Occlusion Problems in Basketball Multi-object Tracking

TL;DR

Basketball-SORT is a novel multi-object tracking method specifically designed for complex occlusion scenarios in basketball videos, utilizing trajectory-based association and scene-specific features to improve accuracy.

Contribution

The paper introduces Basketball-SORT, a new online MOT approach tailored for basketball scenes, addressing complex multi-object occlusion with trajectory and appearance-based methods.

Findings

Achieves a HOTA score of 63.48% on basketball datasets

Outperforms recent MOT algorithms in occlusion handling

Effectively solves complex multi-object occlusion problems

Abstract

Recent deep learning-based object detection approaches have led to significant progress in multi-object tracking (MOT) algorithms. The current MOT methods mainly focus on pedestrian or vehicle scenes, but basketball sports scenes are usually accompanied by three or more object occlusion problems with similar appearances and high-intensity complex motions, which we call complex multi-object occlusion (CMOO). Here, we propose an online and robust MOT approach, named Basketball-SORT, which focuses on the CMOO problems in basketball videos. To overcome the CMOO problem, instead of using the intersection-over-union-based (IoU-based) approach, we use the trajectories of neighboring frames based on the projected positions of the players. Our method designs the basketball game restriction (BGR) and reacquiring Long-Lost IDs (RLLI) based on the characteristics of basketball scenes, and we also solve the occlusion problem based on the player trajectories and appearance features. Experimental results show that our method achieves a Higher Order Tracking Accuracy (HOTA) score of 63.48$\%$ on the basketball fixed video dataset and outperforms other recent popular approaches. Overall, our approach solved the CMOO problem more effectively than recent MOT algorithms.