We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A deep learning method for video‐based action recognition.
- Authors
Zhang, Guanwen; Rao, Yukun; Wang, Changhao; Zhou, Wei; Ji, Xiangyang
- 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.
- Subjects
DEEP learning; HUMAN activity recognition; ARTIFICIAL neural networks; IMAGE fusion; PERFORMANCE evaluation
- Publication
IET Image Processing (Wiley-Blackwell), 2021, Vol 15, Issue 14, p3498
- ISSN
1751-9659
- Publication type
Article
- DOI
10.1049/ipr2.12303