Open AccessA Novel Channel Attention Mechanism for Human Action Recognition Based on Convolutional Kernel
Author(s) -
Xin Shi,
Haiyang Jiang,
Yuanyao Lu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1944/1/012015
Subject(s) - computer science , decoding methods , artificial intelligence , convolutional neural network , kernel (algebra) , channel (broadcasting) , encoding (memory) , action (physics) , action recognition , pattern recognition (psychology) , mechanism (biology) , coding (social sciences) , feature (linguistics) , task (project management) , machine learning , computer vision , algorithm , mathematics , class (philosophy) , computer network , philosophy , statistics , physics , linguistics , management , epistemology , combinatorics , quantum mechanics , economics
With the improvements of computer performance, deep learning has gradually expanded from 2D image tasks to 3D video tasks. Human action recognition is a typical 3D video task, which can achieve category classification by capturing human action characteristics. However, most of the videos are processed by encoding and decoding technology at current, thus the motion details are blurry, which makes it difficult for human action recognition. To solve this problem, we utilize the attention mechanism to “ignore” the blurred feature caused by video coding and decoding technology. Therefore, we hope to embed the attention mechanism in 3D spatiotemporal CNN to overcome this problem. Compared with 3D CNN, the effectiveness of our method is verified on UCF101 and HMDB51 dataset. And our method also proves the convolutional kernel implies channel-wise dependence. Although the improvement of the proposed method is limited, we hope the channel attention mechanism can help people to study neural network.