Multi-views Action Recognition on Deep Learning and K-SVD
Chuanxu Wang, Guofeng Hu, Yun Liu · Journal of Physics Conference Series · 2019
In order to solve the problem of low action accuracy due to changes of view angles, this paper investigates this issue based on deep learning and K-SVD sparse algorithm. Firstly, the paper extracts the feature maps from the different views by convolutional neural networks (CNN) and long short term memory (LSTM), and the extracted feature maps are the multi-views high-level features with semantic information. Secondly, the paper uses the K-SVD sparse algorithm to get the dictionaries corresponding to each views, the dictionaries have very good sparse representations for the features of the action. Then the softmax classifier is used for classification and recognition. The results show that the accuracy are 89.22% and 91.4% on IXMAS datasets and WVU datasets respectively.