Action segmentation in dance videos

Authors:
Han Tingting;Yao Hongxun;Sun Xiaoshuai;Liu Guoyi
Affiliations:
School of Computer Science and Technology, Harbin Institute of Technology, China;School of Computer Science and Technology, Harbin Institute of Technology, China;School of Computer Science and Technology, Harbin Institute of Technology, China;NEC Laboratories China, China
Venue:
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Year:
2012

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Abstract

In this paper, we consider the problem of segmentation of dance videos with unconstrained background. A dynamic saliency detection algorithm is adopted to achieve a fast extraction of the videos' action characteristics, which is robust to the background movements and unexpected distractions. We calculate the saliency of the frame differences and select the maximum within every frame to plot a maximum saliency curve, which reflects the movement along the whole video. After filtered with the frequency filter, the influence of macro body movements is eliminated significantly. We detect the local minimums of the smoothed saliency curve as the boundaries of the segmentations. We test our method on various well annotated dance videos. The experimental results demonstrate the superior performance and robustness of the proposed approach.