Automated classification of cricket pitch frames in cricket video

The automated detection of the cricket pitch in a video recording of a cricket match is a fundamental step in content-based indexing and summarization of cricket videos. In this paper, we propose visualcontent based algorithms to automate the extraction of video frames with the cricket pitch in focus. As a preprocessing step, we first select a subset of frames with a view of the cricket field, of which the cricket pitch forms a part. This filtering process reduces the search space by eliminating frames that contain a view of the audience, close-up shots of specific players, advertisements, etc. The subset of frames containing the cricket field is then subject to statistical modeling of the grayscale (brightness) histogram (SMoG). Since SMoG does not utilize color or domain-specific information such as the region in the frame where the pitch is expected to be located, we propose an alternative algorithm: component quantization based region of interest extraction (CQRE) for the extraction of pitch frames. Experimental results demonstrate that, regardless of the quality of the input, successive application of the two methods outperforms either one applied exclusively. The SMoG-CQRE combination for pitch frame classification yields an average accuracy of 98:6% in the best case (a high resolution video with good contrast) and an average accuracy of 87:9% in the worst case (a low resolution video with poor contrast). Since, the extraction of pitch frames forms the first step in analyzing the important events in a match, we also present a post-processing step, viz. , an algorithm to detect players in the extracted pitch frames

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

Enhanced Augmented Reality Framework for Sports Entertainment Applications

Augmented Reality (AR) superimposes virtual information on real-world data, such as displaying useful information on videos/images of a scene. This dissertation presents an Enhanced AR (EAR) framework for displaying useful information on images of a sports game. The challenge in such applications is robust object detection and recognition. This is even more challenging when there is strong sunlight. We address the phenomenon where a captured image is degraded by strong sunlight. The developed framework consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player detection, face detection, recognition of players, and display of personal information of players. First, an algorithm based on Multi-Scale Retinex (MSR) is proposed for image enhancement. For the tasks of player and face detection, we use adaptive boosting algorithm with Haar-like features for both feature selection and classification. The player face recognition algorithm uses adaptive boosting with the LDA for feature selection and nearest neighbor classifier for classification. The framework can be deployed in any sports where a viewer captures images. Display of players-specific information enhances the end-user experience. Detailed experiments are performed on 2096 diverse images captured using a digital camera and smartphone. The images contain players in different poses, expressions, and illuminations. Player face recognition module requires players faces to be frontal or up to ?350 of pose variation. The work demonstrates the great potential of computer vision based approaches for future development of AR applications.COMSATS Institute of Information Technolog