Human Activities Recognition Based on Skeleton Information via Sparse Representation

Suolan Liu; Lizhi Kong; Hongyuan Wang

doi:10.5626/JCSE.2018.12.1.1

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저널정보

한국정보과학회
Journal of Computing Science and Engineering 학술저널
Journal of Computing Science and Engineering Vol.12 No.1
2018.3 1 - 11 (11page)
DOI : 10.5626/JCSE.2018.12.1.1

저자정보

Suolan Liu (Changzhou University)
Lizhi Kong (Changzhou University)
Hongyuan Wang (Changzhou University)

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초록·키워드

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Human activities recognition is a challenging task due to its complexity of human movements and the variety performed by different subjects for the same action. This paper presents a recognition algorithm by using skeleton information generated from depth maps. Concatenating motion features and temporal constraint feature produces feature vector. Reducing dictionary scale proposes an improved fast classifier based on sparse representation. The developed method is shown to be effective by recognizing different activities on the UTD-MHAD dataset. Comparison results indicate superior performance of our method over some existing methods.

#Activity recognition #Skeleton feature #Temporal feature #Sparse representation