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.

Highlights

  • Techniques related to visual gesture recognition include gesture detection segmentation, tracking positioning, feature extraction, classification recognition, etc

  • This paper proposes a dynamic gesture recognition method based on hidden Markov model (HMM) and D-S evidence theory

  • Firstly, the thesis studies the dynamic gesture motion, and uses the gesture change and motion trajectory as all the features of a complex gesture to extract the tangential angle of the gesture change feature and the palm motion trajectory

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Summary

INTRODUCTION

Techniques related to visual gesture recognition include gesture detection segmentation, tracking positioning, feature extraction, classification recognition, etc. With the development of interactive applications and the improvement of recognition accuracy requirements, a single HMM has been unable to meet the needs of the application, Wang et al [25] Proposed a HMMFNN-based model structure that combines the HMM with a fuzzy neural network to establish a more complex gesture by setting up fuzzy rules It has improved the accuracy, but still has the shortcomings such as the dependence of fuzzy rules on prior experience and insufficient convergence rate due to more hidden layer nodes. This simplifies the dimension of the feature vector, and improves the speed of gesture recognition

MODEL TRAINING
OPTIMAL DYNAMIC GESTURE CLASSIFICATION TRAINING
DYNAMIC GESTURE PARAMETER MODEL TRAINING
TRUST UNCERTAINTY INTERVAL
D-S EVIDENCE COMBINATION RULE
CONCLUSION
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