Highlight generation of cricket match using deep learning

Abstract

In this paper, we propose various algorithms to generate highlight of a cricket video by detecting important events such as replay, pitch view, boundary view, bowler, batsman, umpire, spectator, player’s gathering etc. The proposed method work in two parts. The first part contains five levels. At level one key frame are identified by using hue histogram difference. At level two classify frames to replay frames or real-time frames by detecting the absence of scoreboard. At level three real time frames are classified into field view or non field view based on Dominant Grass Pixel Ration. At level 4a onfield frames are classified into pitch view and boundary view. At level 4b by using edge detection method close-up and crowd frames are detected. Level 5a and 5b again divide each close-up and crowd frames into umpire, bowler, batsman, players gathering and spectator. Concept mining is done on the second part by using the Apriori algorithm and labeled frame events are input. Then combines all the concept detected to form a summarized video. Results at the end of paper show the accuracy of our approach.