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There has been a significant increase in the adoption of technology incricket recently. This trend has created the problem of duplicate work beingdone in similar computer vision-based research works. Our research tries tosolve one of these problems by segmenting ball deliveries in a cricketbroadcast using deep learning models, MobileNet and YOLO, thus enablingresearchers to use our work as a dataset for their research. The output fromour research can be used by cricket coaches and players to analyze balldeliveries which are played during the match. This paper presents an approachto 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 sceneof the video. Object detection models are applied to reach a high level ofaccuracy in terms of correctly extracting video shots. The proof of concept forbuilding large datasets of video shots for ball deliveries is proposed whichpaves the way for further processing on those shots for the extraction ofsemantics. Ball tracking in these video shots is also done using a separateRetinaNet model as a sample of the usefulness of the proposed dataset. Theposition on the cricket pitch where the ball lands is also extracted bytracking the ball along the y-axis. The video shot is then classified as afull-pitched, good-length or short-pitched delivery.

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