A Novel Technique for Space-Time-Interest Point Detection and Description for Dance Video Classification

Abstract

This paper presents a different type of video analysis problem which is cultural activity analysis in general and Indian Classical Dance (ICD) classification in particular. To tackle this problem we propose a novel method for space time interest point (STIP) detection and description using differential geometry. Each video is represented by sparse code of STIP descriptors in each frame and then classification is done by a non-linear SVM with χ 2-kernel. We have created a ICD dataset of six classes (Bharatanatyam, Kathak, Kuchipudi, Mohiniyattam, Manipuri and Odissi) from YouTube and got on an average 68.18% accuracy which is better than the performance of state-of-the-art general human activity classification methods. We also have tested our algorithm on the benchmark datasets, like UCF sports and KTH, and the accuracy is comparable to that of the state-of-the-art.KeywordsVideo ClipAction RecognitionInterest PointGolf SwingVisual VocabularyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.