Name
Abstract | ||
|---|---|---|
In recent years, human posture recognition based on Kinect gradually has been paid more attention. However, the current researches and methods have drawbacks, such as low recognition accuracy and less recognizable postures. This paper proposed a novel method. The method utilized image processing technique, BP neural network technique, skeleton data and depth data captured by Kinect v2 to recognize postures. We distinguished four types of postures (sitting cross-legged, kneeling or sitting, standing, and other postures) by using the natural ratios of human body parts, and judged the kneeling and sitting postures by calculating the 3D spatial relation of the feature points. Finally, we applied BP neural network to recognize the lying and bending postures. The experimental results indicated that the robustness and timeliness of our method was strong, the recognition accuracy was 98.98%. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-7299-4_27 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Human posture recognition,Kinect v2,Depth data,Skeleton data,Image processing,BP neural network | Conference | 771 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 5 | |