automatic object and event detection in field hockey videos using deep learning techniques

Abstract

newline quotField hockey, a dynamic outdoor team sport, undergoes increasing analysis through videos to newlineglean insights into player performance and game strategies. Manual analysis, however, is newlinefraught with subjectivity and demands significant time and effort. To address these challenges, newlinethis research endeavours to develop an automated system employing deep learning techniques newlinefor object and event detection in field hockey videos. The primary objective is to enhance the newlineaccuracy and objectivity of video analysis, thereby revolutionizing the approach to newlineunderstanding the sport. By leveraging advanced technology, such as convolutional neural newlinenetworks (CNNs), the research aims to automate the identification and categorization of key newlineobjects and events in field hockey gameplay. This shift towards automation promises to newlinestreamline the analysis process and provide more reliable insights for coaches, players, newlineanalysts, and audiences. Overall, the research seeks to bridge the gap between manual analysis newlineand technological innovation in the field of sports video analysis.quot newline