TY - JOUR
T1 - Dynamic gesture recognition in the Internet of Things
AU - Li, Gongfa
AU - Wu, Hao
AU - Jiang, Guozhang
AU - Xu, Shuang
AU - Liu, Honghai
PY - 2018/12/18
Y1 - 2018/12/18
N2 - 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 Internet of Things technology. Human-computer interaction through gestures is the direction of continuous research on Internet of Things 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 Internet of Things 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 DS evidence theory is proposed. Base 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 Internet of Things technology.
AB - 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 Internet of Things technology. Human-computer interaction through gestures is the direction of continuous research on Internet of Things 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 Internet of Things 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 DS evidence theory is proposed. Base 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 Internet of Things technology.
KW - D-S evidence theory
KW - Dynamics
KW - Feature extraction
KW - Gesture recognition
KW - Hidden Markov model (HMM)
KW - Hidden Markov models
KW - Internet of Things
KW - Internet of Things (IoT)
KW - Tracking
KW - Trajectory
UR - http://www.scopus.com/inward/record.url?scp=85058875515&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2018.2887223
DO - 10.1109/ACCESS.2018.2887223
M3 - Article
AN - SCOPUS:85058875515
SN - 2169-3536
VL - 7
SP - 23713
EP - 23724
JO - IEEE Access
JF - IEEE Access
ER -