Abstract
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.
Highlights
As the degree of verisimilitude that can be obtained from motion capture techniques increases, the significance and applicability of motion capture have increased
This paper focuses on two main aspects: first, an automatic key-frame extraction technique was developed based on the adaptive threshold
More than 100 real human motion sequences of different motion types were captured at a frame rate of 120 Hz from CMU as our testing collection and our method was implemented in Matlab® which runs on a Core(TM) 2 2.4 GHz computer with 4G memory
Summary
An Efficient Method of Key-Frame Extraction Based on a Cluster Algorithm by Qiang Zhang, Shao-Pei Yu1,Dong-Sheng Zhou, Xiao-Peng Wei. 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. The present work 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
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