期刊
IET IMAGE PROCESSING
卷 14, 期 11, 页码 2480-2486出版社
WILEY
DOI: 10.1049/iet-ipr.2019.1248
关键词
learning (artificial intelligence); video signal processing; feature extraction; gesture recognition; image classification; image fusion; recurrent neural nets; convolutional neural nets; image sequences; spatiotemporal phenomena; human computer interaction; variant ConvLSTM; human communication; human-computer interaction; dynamic gesture recognition method; deep learning; spatiotemporal feature extraction; gesture recognition architecture; feature fusion network; local aspects; global aspects; deep aspects; local spatiotemporal feature information; 3D residual network; channel feature fusion; global spatiotemporal information; multifeature fusion depthwise separable network; higher-level features; depth feature information; SKIG dataset; Sheffifield Kinect Gesture dataset; video sequence; gesture feature information; variant convolutional long short-term memory; Jester dataset; classification accuracies
类别
资金
- National Key Research and Development Program of China [2018YFB1306900]
- National Natural Science Foundation of China
Gesture is a natural form of human communication, and it is of great significance in human-computer interaction. In the dynamic gesture recognition method based on deep learning, the key is to obtain comprehensive gesture feature information. Aiming at the problem of inadequate extraction of spatiotemporal features or loss of feature information in current dynamic gesture recognition, a new gesture recognition architecture is proposed, which combines feature fusion network with variant convolutional long short-term memory (ConvLSTM). The architecture extracts spatiotemporal feature information from local, global and deep aspects, and combines feature fusion to alleviate the loss of feature information. Firstly, local spatiotemporal feature information is extracted from video sequence by 3D residual network based on channel feature fusion. Then the authors use the variant ConvLSTM to learn the global spatiotemporal information of dynamic gesture, and introduce the attention mechanism to change the gate structure of ConvLSTM. Finally, a multi-feature fusion depthwise separable network is used to learn higher-level features including depth feature information. The proposed approach obtains very competitive performance on the Jester dataset with the classification accuracies of 95.59%, achieving state-of-the-art performance with 99.65% accuracy on the SKIG (Sheffifield Kinect Gesture) dataset.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据