Abstract
In this paper, we propose a novel motion descriptor Seg-SIFT-ACC for human action recognition. The proposed descriptor is based both on the accordion representation of the video and its temporal segmentation into elementary motion segments. The accordion representation aims to put in space adjacency the columns of the video frames having a high temporal correlation. For complex videos containing many different elementary actions, the accordion representation may put in spatial adjacency temporally correlated pixels that belong to different elementary actions. To surmount this problem, we divide the video into elementary motions segments and we apply the accordion representation on each one separately. © 2013 Springer-Verlag.
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CITATION STYLE
Sekma, M., Mejdoub, M., & Ben Amar, C. (2013). Human action recognition using temporal segmentation and accordion representation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8048 LNCS, pp. 563–570). https://doi.org/10.1007/978-3-642-40246-3_70
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