OSL-ActionSpotting: A Unified Library for Action Spotting in Sports Videos

TL;DR

OSL-ActionSpotting is a unified Python library that consolidates multiple state-of-the-art action spotting algorithms for sports videos, improving research efficiency and practical application in sports analytics.

Contribution

The paper introduces a modular, unified library that integrates various action spotting methods, maintaining their performance and enhancing usability for sports video analysis.

Findings

Successfully integrated three action spotting methods into a single framework.

Achieved performance metrics comparable to original, separate implementations.

Enhanced usability and accessibility for sports analytics researchers and practitioners.

Abstract

Action spotting is crucial in sports analytics as it enables the precise identification and categorization of pivotal moments in sports matches, providing insights that are essential for performance analysis and tactical decision-making. The fragmentation of existing methodologies, however, impedes the progression of sports analytics, necessitating a unified codebase to support the development and deployment of action spotting for video analysis. In this work, we introduce OSL-ActionSpotting, a Python library that unifies different action spotting algorithms to streamline research and applications in sports video analytics. OSL-ActionSpotting encapsulates various state-of-the-art techniques into a singular, user-friendly framework, offering standardized processes for action spotting and analysis across multiple datasets. We successfully integrated three cornerstone action spotting methods into OSL-ActionSpotting, achieving performance metrics that match those of the original, disparate codebases. This unification within a single library preserves the effectiveness of each method and enhances usability and accessibility for researchers and practitioners in sports analytics. By bridging the gaps between various action spotting techniques, OSL-ActionSpotting significantly contributes to the field of sports video analysis, fostering enhanced analytical capabilities and collaborative research opportunities. The scalable and modularized design of the library ensures its long-term relevance and adaptability to future technological advancements in the domain.