21 May 2015 Skeleton-based human action recognition using multiple sequence alignment
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Human action recognition and analysis is an active research topic in computer vision for many years. This paper presents a method to represent human actions based on trajectories consisting of 3D joint positions. This method first decompose action into a sequence of meaningful atomic actions (actionlets), and then label actionlets with English alphabets according to the Davies-Bouldin index value. Therefore, an action can be represented using a sequence of actionlet symbols, which will preserve the temporal order of occurrence of each of the actionlets. Finally, we employ sequence comparison to classify multiple actions through using string matching algorithms (Needleman-Wunsch). The effectiveness of the proposed method is evaluated on datasets captured by commodity depth cameras. Experiments of the proposed method on three challenging 3D action datasets show promising results.
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Wenwen Ding, Wenwen Ding, Kai Liu, Kai Liu, Fei Cheng, Fei Cheng, Jin Zhang, Jin Zhang, YunSong Li, YunSong Li, } "Skeleton-based human action recognition using multiple sequence alignment", Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010N (21 May 2015); doi: 10.1117/12.2180828; https://doi.org/10.1117/12.2180828

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