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 χ2-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. © 2013 Springer-Verlag.