Cricket Video Events Recognition using HOG, LBP and Multi-class SVM

Abstract

The world has witnessed a growth in multimedia data, especially video data over the past few years due to increased internet bandwidth and higher processing power of computers. Having a large number of video data also require techniques to store, summarize, index and information retrieval. More attention has been given in recent years to develop techniques which summarize, index and retrieve sports videos due to its commercial aspects. This paper proposes a framework which classifies a cricket video into one of the four events namely Bowled Out, Caught Behind, Catch Out and LBW Out. The framework uses training videos from each category of event and summarizes the videos into key frames. HOG and LBP features are computed for key frames and fused to form a single feature vector which will be labeled accordingly which represents a single event in video. Feature vector is given to a Multi-Class SVM which classifies the video into one of the four events. The experimental results show that the Precision of our technique is 77.23%, Recall is 77.86%, F-Measure is 77.55% and the Accuracy is 65.62%. The evaluation metrics of our technique are promising, because in the literature there is no other technique present so far for event detection & classification in cricket videos.