A Survey on Content-Aware Video Analysis for Sports

TL;DR

This survey reviews recent advances in content-aware sports video analysis, emphasizing semantic understanding and content structure to improve information access and user engagement in sports broadcasts.

Contribution

It provides a comprehensive overview of content-aware analysis techniques, hierarchical content modeling, and discusses future trends and challenges in sports video analysis.

Findings

Content-aware methods bridge sensation and excitement effectively.

Hierarchical content models enhance sports video understanding.

Identifies key trends and challenges for future research.

Abstract

Sports data analysis is becoming increasingly large-scale, diversified, and shared, but difficulty persists in rapidly accessing the most crucial information. Previous surveys have focused on the methodologies of sports video analysis from the spatiotemporal viewpoint instead of a content-based viewpoint, and few of these studies have considered semantics. This study develops a deeper interpretation of content-aware sports video analysis by examining the insight offered by research into the structure of content under different scenarios. On the basis of this insight, we provide an overview of the themes particularly relevant to the research on content-aware systems for broadcast sports. Specifically, we focus on the video content analysis techniques applied in sportscasts over the past decade from the perspectives of fundamentals and general review, a content hierarchical model, and trends and challenges. Content-aware analysis methods are discussed with respect to object-, event-, and context-oriented groups. In each group, the gap between sensation and content excitement must be bridged using proper strategies. In this regard, a content-aware approach is required to determine user demands. Finally, the paper summarizes the future trends and challenges for sports video analysis. We believe that our findings can advance the field of research on content-aware video analysis for broadcast sports.