A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

Abstract

Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting the trajectories of player or ball, recognizing the teams strategies, classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researchers views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been provided. This work reviews a detailed discussion on some of the artificial intelligence(AI)applications in sports vision, GPU-based work stations, and embedded platforms. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.

References in corpus (16)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Recurrent Neural Network Regularization
• A Survey on Content-Aware Video Analysis for Sports
• Generating Long-term Trajectories Using Deep Hierarchical Networks
• DEFT: Detection Embeddings for Tracking
• Self-Supervised Small Soccer Player Detection and Tracking
• Pose is all you need: The pose only group activity recognition system (POGARS)
• Player Identification in Hockey Broadcast Videos
• QPass: a Merit-based Evaluation of Soccer Passes
• Enhancing Trajectory Prediction using Sparse Outputs: Application to Team Sports
• Evaluating Soccer Player: from Live Camera to Deep Reinforcement Learning
• Monocular Quasi-Dense 3D Object Tracking
• Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos
• Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model
• You Cannot Do That Ben Stokes: Dynamically Predicting Shot Type in Cricket Using a Personalized Deep Neural Network
• Indoor Activity Detection and Recognition for Sport Games Analysis

Cited by in corpus (3)

• PLayerTV: Advanced Player Tracking and Identification for Automatic Soccer Highlight Clips
• An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment
• Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives