This paper proposes a novel method of key-frame extraction for use with motion capture data. This method is based on an unsupervised cluster algorithm. First, the motion sequence is clustered into two classes by the similarity distance of the adjacent frames so that the thresholds needed in the next step can be determined adaptively. Second, a dynamic cluster algorithm called ISODATA is used to cluster all the frames and the frames nearest to the center of each class are automatically extracted as key-frames of the sequence. Unlike many other clustering techniques, the present improved cluster algorithm can automatically address different motion types without any need for specified parameters from users. The proposed method is capable of summarizing motion capture data reliably and efficiently. The present work also provides a meaningful comparison between the results of the proposed key-frame extraction technique and other previous methods. These results are evaluated in terms of metrics that measure reconstructed motion and the mean absolute error value, which are derived from the reconstructed data and the original data.
Baak A, Müeller M, Seidel HP. An efficient algorithm for keyframe-based motion retrieval in the presence of temporal deformations. New York: 1st ACM international Conference on Multimedia Information Retrieval, 451-458; 2008
Bulut E, Capin T. Keyframe extraction from motion capture data by curve saliency. Belgium: Proceedings of 20th Annual Conference on Comput Animat and Social Agents, 63-67; 2007
Clifford KFS, Baciu G. Entropy-based Motion Extraction for Motion Capture Animation: Motion Capture and Retrieval. Comput Animat Virt W, 2005; 16(3-4): 225-235
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SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.