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

A Study On Information Retrieval Systems

A video is a key component of today's multimedia applications,  including Video Cassette Recording (VCR), Video-on-Demand (VoD), and virtual walkthrough. This happens supplementary with the fast amplification in video skill (Rynson W.H. Lau et al. 2000). Owing to innovation's progress in the  media, computerized TV, and data frameworks, an immense measure of video information is now exhaustively realistic (Walid G. Aref et al. 2003). The startling advancement in computerized video content has made entrée and moves the data in a tremendous video database a muddled and sensible issue (Chih-Wen Su et al. 2005). Therefore, the necessity for creating devices and frameworks that can effectively investigate the most needed video content, has evoked a great deal of interest among analysts. Sports video has been chosen as the prime application in this proposition since it has attracted viewers around the world

A Literature Study On Video Retrieval Approaches

A detailed survey has been carried out to identify the various research articles available in the literature in all the categories of video retrieval and to do the analysis of the major contributions and their advantages, following are the literature used for the assessment of the state-of-art work on video retrieval. Here, a large number of papershave been studied

Cricket Player Profiling: Unraveling Strengths and Weaknesses Using Text Commentary Data

Devising player-specific strategies in cricket necessitates a meticulous understanding of each player's unique strengths and weaknesses. Nevertheless, the absence of a definitive computational approach to extract such insights from cricket players poses a significant challenge. This paper seeks to address this gap by establishing computational models designed to extract the rules governing player strengths and weaknesses, thereby facilitating the development of tailored strategies for individual players. The complexity of this endeavor lies in several key areas: the selection of a suitable dataset, the precise definition of strength and weakness rules, the identification of an appropriate learning algorithm, and the validation of the derived rules. To tackle these challenges, we propose the utilization of unstructured data, specifically cricket text commentary, as a valuable resource for constructing comprehensive strength and weakness rules for cricket players. We also introduce computationally feasible definitions for the construction of these rules, and present a dimensionality reduction technique for the rule-building process. In order to showcase the practicality of this approach, we conduct an in-depth analysis of cricket player strengths and weaknesses using a vast corpus of more than one million text commentaries. Furthermore, we validate the constructed rules through two distinct methodologies: intrinsic and extrinsic. The outcomes of this research are made openly accessible, including the collected data, source code, and results for over 250 cricket players, which can be accessed at https://bit.ly/2PKuzx8.Comment: The initial work was published in the ICMLA 2019 conferenc