A Survey on Content-Aware Video Analysis for Sports (1703.01170v1)

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.

• The paper introduces a content pyramid framework that organizes sports video content from raw data to high-level semantics.
• It examines deep learning approaches for object detection and action recognition to capture complex player interactions.
• The study outlines methods for real-time event detection and contextual inference to enhance sports analytics and viewing experiences.

Content-Aware Video Analysis for Sports: An Overview

The paper, "A Survey on Content-aware Video Analysis for Sports" by Huang-Chia Shih, provides an extensive review of content-aware video analysis techniques applied to sports broadcasts. This work diverges from traditional spatiotemporal analysis by prioritizing the content-based and semantic aspects of sports video data. It explores the methodologies and challenges of analyzing video content in sports from three key perspectives: object detection and action recognition, highlight detection and event recognition, and contextual inference and semantic analysis.

Content Pyramid and Sports Genre Classification

The concept of a content pyramid is central to organizing sports video content for analysis. This framework ranks video information hierarchically from raw data to high-level semantics, allowing the identification of key components such as objects, actions, and events. The paper classifies sports into field sports, racket sports, and posture sports, noting that each category has distinct visual features and analytic needs. The researchers emphasize the importance of genre classification as a preliminary step in managing sports media, evidencing this with a tree structure of various sports genres.

Object Detection and Action Recognition

Object detection and action recognition are crucial for interpreting individual and collective movements within sports videos. Shih explores techniques such as tracking object trajectories and recognizing actions through body posture analysis. The paper highlights methods using deep neural networks and dense trajectories for more precise recognition of complex interactions, pushing towards a more accurate and comprehensive understanding of player actions. This advancement mirrors the broader trend in machine learning towards leveraging deep learning for improving predictive accuracy in dynamic environments.

Highlight Detection and Event Recognition

The paper provides a detailed account of strategies for detecting key moments, like game highlights, by segmenting video based on scene changes and applying play-and-break analysis. The use of model analytics, such as hidden Markov models (HMM) and support vector machines (SVM), is prevalent in this domain. The research underscores the need for refined methodologies capable of real-time processing to detect events that can enhance spectator experience or aid in strategic analysis by teams.

Contextual Inference and Semantic Analysis

The paper further investigates the extraction of context and semantics from sports videos, focusing on using superimposed captions and visual cues to obtain game summaries and stat insights. The integration of context enhances the semantic inference of video content, expanding the potential applications of video analysis systems. The authors propose that future research should focus on refining caption interpretation and contextual inference to increase automation and accuracy in sports analytics.

Challenges and Future Directions

Shih identifies key challenges facing sports video analysis, including the need for scalable, cloud-based solutions that can handle the growing amount of user-generated content and metadata. There is a particular emphasis on the potential of cloud computing and mobile media networks to revolutionize content delivery. Additionally, the paper suggests that advancements in large-scale machine learning and more robust feature extraction methodologies are essential for improving the precision of video content analysis.

Conclusion

This survey explores the intersection of content-aware systems and sports video analysis, advocating for advancements that rigorously interpret semantic content to meet increasing user demands. The research highlights the complexity of video data, presenting challenges in both technical and application domains. Future developments in AI and machine learning are expected to further enhance the capacity to not just capture and process video data effectively but also to deliver insightful analytics that drive engagement and strategic insights within the sports industry.