Online Multiple Athlete Tracking with Pose-Based Long-Term Temporal Dependencies
| Autor: | Mengxiao Zhu, Rui He, Qingjie Liu, Nan Ran, Longteng Kong |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Multi-Athlete Tracking (MAT)
Memory Long-Term Computer science long short-term memory (LSTM) networks ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre Tracking (particle physics) lcsh:Chemical technology 01 natural sciences Biochemistry Article Motion (physics) Analytical Chemistry Task (project management) Motion Similarity (psychology) 0202 electrical engineering electronic engineering information engineering Humans lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Blossom algorithm 0105 earth and related environmental sciences sports video analysis business.industry Atomic and Molecular Physics and Optics Term (temporal) Athletes 020201 artificial intelligence & image processing Artificial intelligence business computer Algorithms Sports |
| Zdroj: | Sensors, Vol 21, Iss 197, p 197 (2021) Sensors (Basel, Switzerland) Sensors Volume 21 Issue 1 |
| ISSN: | 1424-8220 |
| Popis: | 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&rsquo 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. |
| Databáze: | OpenAIRE |
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