Deep-Learning-Based Computer Vision Approach For The Segmentation Of Ball Deliveries And Tracking In Cricket

There has been a significant increase in the adoption of technology in cricket recently. This trend has created the problem of duplicate work being done in similar computer vision-based research works. Our research tries to solve one of these problems by segmenting ball deliveries in a cricket broadcast using deep learning models, MobileNet and YOLO, thus enabling researchers to use our work as a dataset for their research. The output from our research can be used by cricket coaches and players to analyze ball deliveries which are played during the match. This paper presents an approach to segment and extract video shots in which only the ball is being delivered. The video shots are a series of continuous frames that make up the whole scene of the video. Object detection models are applied to reach a high level of accuracy in terms of correctly extracting video shots. The proof of concept for building large datasets of video shots for ball deliveries is proposed which paves the way for further processing on those shots for the extraction of semantics. Ball tracking in these video shots is also done using a separate RetinaNet model as a sample of the usefulness of the proposed dataset. The position on the cricket pitch where the ball lands is also extracted by tracking the ball along the y-axis. The video shot is then classified as a full-pitched, good-length or short-pitched delivery

Virtual camera synthesis for soccer game replays

International audienceIn this paper, we present a set of tools developed during the creation of a platform that allows the automatic generation of virtual views in a live soccer game production. Observing the scene through a multi-camera system, a 3D approximation of the players is computed and used for the synthesis of virtual views. The system is suitable both for static scenes, to create bullet time effects, and for video applications, where the virtual camera moves as the game plays

A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification

Action spotting in soccer videos is the task of identifying the specific time when a certain key action of the game occurs. Lately, it has received a large amount of attention and powerful methods have been introduced. Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. Most approaches have focused on the latter, given that their models exploit the global visual features of the sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification, or player classification task, we obtain an accuracy of 97.72% in our annotated benchmark. For the action spotting task, our method obtains an overall performance of 57.83% average-mAP by combining it with other audiovisual modalities. This performance surpasses similar graph-based methods and has competitive results with heavy computing methods. Code and data are available at https://github.com/IPCV/soccer_action_spotting.Comment: Accepted at the 5th International ACM Workshop on Multimedia Content Analysis in Sports (MMSports 2022

A New Action Recognition Framework for Video Highlights Summarization in Sporting Events

To date, machine learning for human action recognition in video has been widely implemented in sports activities. Although some studies have been successful in the past, precision is still the most significant concern. In this study, we present a high-accuracy framework to automatically clip the sports video stream by using a three-level prediction algorithm based on two classical open-source structures, i.e., YOLO-v3 and OpenPose. It is found that by using a modest amount of sports video training data, our methodology can perform sports activity highlights clipping accurately. Comparing with the previous systems, our methodology shows some advantages in accuracy. This study may serve as a new clipping system to extend the potential applications of the video summarization in sports field, as well as facilitates the development of match analysis system.Comment: 18 pages, 3 figures, 4 table