Temporal classification of events in cricket videos

Video search today uses the metadata surrounding the video, ignoring its semantic content. Over the years, a lot of research has gone into indexing and browsing of sports video content. In this work, we present a novel approach for classification of events in cricket videos and thus, summarize its visual content. The proposed method segments a cricket video into shots and identifies the visual content in them. Using sequential pattern mining and support vector machine, we classify the sequence of shots into four events, namely RUN, FOUR, SIX and OUT. The cricket video is then summarized based on user-supplied parameters. The performance of the system has been tested on a number of cricket video clips and was found to have an accuracy of 87.8%.

I Introduction

Video mining aims at extracting semantic structures from a video and thus enable summarization, browsing and indexing of the video content [5]. There has been a lot of research on sports video annotation and summarization, especially for games such as base ball, soccer, foot ball, tennis, etc [2], [5], [8], [16]. Compared to other sports, less work has been done on cricket videos [6], [14], [17], [18], probably due to the increased complexity of the game and long duration of the matches [6].