A deep learning method for video‐based action recognition

Autor: Guanwen Zhang, Yukun Rao, Changhao Wang, Wei Zhou, Xiangyang Ji
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: IET Image Processing, Vol 15, Iss 14, Pp 3498-3511 (2021)
Druh dokumentu: article
ISSN: 1751-9667
1751-9659
DOI: 10.1049/ipr2.12303
Popis: Abstract In this paper, a deep learning method for video‐based action recognition is proposed. On the one hand, boundary compensation on the basis of a deep neural network is performed to achieve action proposal. Boundary compensation considering non‐maximum suppression according to sliding window priority is applied to remove redundant windows. To accurately detect boundaries, a boundary compensation network is established with multiple networks to process different numbers of segments. On the other hand, action recognition based on the resultant action proposals is performed. To further utilise boundary compensation, three methods are introduced for key frame selection. Optical flow and RGB features are combined via a channel fusion to realise feature representation. A two‐stream network with a spatiotemporal structure is adopted for action recognition. The proposed method is evaluated on three public datasets. The experimental results demonstrate that the proposed method achieves a superior performance to that of state‐of‐the‐art methods.
Databáze: Directory of Open Access Journals
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