Scaling and occlusion robust athlete tracking in sports videos

Lu, Jianghu; Huang, Di; Wang, Yunhong; Kong, Longteng

doi:10.1109/icassp.2016.7471932

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…In the past few decades, despite a few works focusing on single athlete tracking [ 11 ], most studies address the Multi-Athlete Tracking problem. Some early attempts make use of specific clues in sports fields (e.g., site boundaries) to facilitate athlete tracking.…”

Section: Related Workmentioning

confidence: 99%

Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies

Kong

Zhu

Ran

et al. 2020

Sensors

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This paper addresses the Multi-Athlete Tracking (MAT) problem, which plays a crucial role in sports video analysis. There exist specific challenges in MAT, e.g., athletes share a high similarity in appearance and frequently occlude with each other, making existing approaches not applicable for this task. To address this problem, we propose a novel online multiple athlete tracking approach which make use of long-term temporal pose dynamics for better distinguishing different athletes. Firstly, we design a Pose-based Triple Stream Network (PTSN) based on Long Short-Term Memory (LSTM) networks, capable of modeling long-term temporal pose dynamics of athletes, including pose-based appearance, motion and athletes’ interaction clues. Secondly, we propose a multi-state online matching algorithm based on bipartite graph matching and similarity scores produced by PTSN. It is robust to noisy detections and occlusions due to the reliable transitions of multiple detection states. We evaluate our method on the APIDIS, NCAA Basketball and VolleyTrack databases, and the experiment results demonstrate its effectiveness.

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Section: Related Workmentioning

confidence: 99%

Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies

Kong

Zhu

Ran

et al. 2020

Sensors

Self Cite

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A Robust Multi-Athlete Tracking Algorithm by Exploiting Discriminant Features and Long-Term Dependencies

Ran

Kong

Wang

et al. 2018

Lecture Notes in Computer Science

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3D zebrafish tracking with topology association

Jin

Zhang

et al. 2022

IET Image Processing

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Recently, zebrafish has received more and more attention due to its wide range of applications such as regeneration promoting therapeutics and drug discovery. Therefore, vision‐based trackers are utilized to record the swimming trajectory of zebrafish. In this paper, a re‐association method is introduced in the 3D reconstruction process to generate missed targets caused by occlusion. Since the variation of the overall tracking targets has the property of continuity and stability, a topology association model (TAM) is proposed by point group similarity into the tracking framework. TAM describes the movement of zebrafish from the macroscopic level and utilizes the changes of the point group structure for tracking. Experimental results show that the tracking framework enhances the overall performance and promotes the trajectory integrity. On the latest 3D‐ZeF20 benchmark, state‐of‐the‐art results are achieved. In addition, TAM tracking framework is applied to 2D general tracking to prove that the method is useful and have great advantage in other scenarios with relatively stable amount of targets as well.

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Scaling and occlusion robust athlete tracking in sports videos

Cited by 5 publications

References 18 publications

Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies

Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies

A Robust Multi-Athlete Tracking Algorithm by Exploiting Discriminant Features and Long-Term Dependencies

3D zebrafish tracking with topology association

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