# A Survey on Content-Aware Video Analysis for Sports

arXiv: https://arxiv.org/abs/1703.01170 · HTML: https://mlsanity.com/paper/1703.01170/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2017.

## Built on by (most cited first, 27 of 27)

- [A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications](https://mlsanity.com/paper/2206.01038/) (2022)
- [A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions](https://mlsanity.com/paper/2203.02281/) (2022)
- [SoccerNet-Echoes: A Soccer Game Audio Commentary Dataset](https://mlsanity.com/paper/2405.07354/) (2024)
- [Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement](https://mlsanity.com/paper/2308.15816/) (2023)
- [Smart Director: An Event-Driven Directing System for Live Broadcasting](https://mlsanity.com/paper/2201.04024/) (2022)
- [Towards cumulative race time regression in sports: I3D ConvNet transfer learning in ultra-distance running events](https://mlsanity.com/paper/2208.11191/) (2022)
- [FSBench: A Figure Skating Benchmark for Advancing Artistic Sports Understanding](https://mlsanity.com/paper/2504.19514/) (2025)
- [Slow Motion Matters: A Slow Motion Enhanced Network for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2211.11324/) (2022)
- [DIAMOND: An LLM-Driven Agent for Context-Aware Baseball Highlight Summarization](https://mlsanity.com/paper/2506.02351/) (2025)
- [EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos](https://mlsanity.com/paper/2101.04954/) (2021)
- [Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skating](https://mlsanity.com/paper/2307.02730/) (2023)
- [Classifying Soccer Ball-on-Goal Position Through Kicker Shooting Action](https://mlsanity.com/paper/2312.15236/) (2023)
- [Efficient Global Optimization of Non-differentiable, Symmetric Objectives for Multi Camera Placement](https://mlsanity.com/paper/2103.11210/) (2021)
- [Video-based Analysis of Soccer Matches](https://mlsanity.com/paper/2105.04875/) (2021)
- [Context-aware Visual Tracking with Joint Meta-updating](https://mlsanity.com/paper/2204.01513/) (2022)
- [Do We Need Large VLMs for Spotting Soccer Actions?](https://mlsanity.com/paper/2506.17144/) (2025)
- [Predicting Soccer Penalty Kick Direction Using Human Action Recognition](https://mlsanity.com/paper/2507.12617/) (2025)
- [Enhancing Multi-Camera Gymnast Tracking Through Domain Knowledge Integration](https://mlsanity.com/paper/2511.16532/) (2025)
- [OnPoint: Offline-to-Online Multi-Level Distillation for Point-Supervised Online Temporal Action Localization](https://mlsanity.com/paper/2607.00289/) (2026)
- [A Survey of Performance Optimization in Neural Network-Based Video Analytics Systems](https://mlsanity.com/paper/2105.14195/) (2021)
- [PLayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips](https://mlsanity.com/paper/2407.16076/) (2024)
- [A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications](https://mlsanity.com/paper/2507.15628/) (2025)
- [TrajSV: A Trajectory-based Model for Sports Video Representations and Applications](https://mlsanity.com/paper/2508.11569/) (2025)
- [Video Understanding by Design: How Datasets Shape Video Models](https://mlsanity.com/paper/2509.09151/) (2025)
- [Automated Wicket-Taking Delivery Segmentation and Trajectory-Based Dismissal-Zone Analysis in Cricket Videos Using OCR-Guided YOLOv8](https://mlsanity.com/paper/2510.18405/) (2025)
- [Breakdance Video classification in the age of Generative AI](https://mlsanity.com/paper/2510.20287/) (2025)
- [EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing](https://mlsanity.com/paper/2605.08723/) (2026)

## Abstract (arXiv)

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.