Name
Abstract | ||
|---|---|---|
Gesture recognition based on computer vision has gradually become a hot research direction in the field of human-computer interaction. The field of human-computer interaction is an important direction in the Internet of Things (IoTs) technology. Human-computer interaction through gestures is the direction of continuous research on IoTs technology. In recent years, the Kinect sensor-based gesture recognition method has been widely used in gesture recognition, because it can separate gestures from complex backgrounds and is less affected by illumination and can accurately track and locate gesture motions. At present, the Kinect sensor needs to be further improved on the recognition of complex gesture movements, especially the problem that the recognition rate of dynamic gestures is not high, which hinders the development of human-computer interaction under the IoTs technology. In this paper, based on the above problems, the Kinect-based gesture recognition is analyzed in detail, and a dynamic gesture recognition method based on HMM and D-S evidence theory is proposed. Based on the original HMM, the tangent angle and gesture change at different moments of the palm trajectory are used as the characteristics of the complex motion gesture, and the dimension of the trajectory tangent is reduced by the number of quantization codes. Then, the parameter model training of HMM is completed. Finally, combined with D-S evidence theory, combinatorial logic is judged, dynamic gesture recognition is carried out, and a better recognition effect is obtained, which lays a good foundation for human-computer interaction under the IoTs technology. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2018.2887223 | IEEE ACCESS |
Keywords | Field | DocType |
Gesture recognition,hidden Markov model (HMM),D-S evidence theory,Internet of Things (IoT) | Computer vision,Computer science,Gesture,Gesture recognition,Feature extraction,Combinational logic,Tangent,Artificial intelligence,Quantization (signal processing),Hidden Markov model,Trajectory,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 6 |
PageRank | References | Authors |
0.41 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gongfa Li | 1 | 239 | 43.45 |
| Hao Wu | 2 | 6 | 0.75 |
| Guozhang Jiang | 3 | 172 | 27.25 |
| Shuang Xu | 4 | 274 | 32.53 |
| Honghai Liu | 5 | 1974 | 178.69 |