Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network

Zhonghua Xia,Changzai Wang,Jinming Xing,Xiaofeng Li,Sang-Bing Tsai

doi:10.1155/2021/2621691

Abstract

There are some problems in the current human motion target gesture recognition algorithms, such as classification accuracy, overlap ratio, low recognition accuracy and recall, and long recognition time. A gesture recognition algorithm of human motion based on deep neural network was proposed. First, Kinect interface equipment was used to collect the coordinate information of human skeleton joints, extract the characteristics of motion gesture nodes, and construct the whole structure of key node network by using deep neural network. Second, the local recognition region was introduced to generate high-dimensional feature map, and the sampling kernel function was defined. The minimum space-time domain of node structure map was located by sampling in the space-time domain. Finally, the deep neural network classifier was constructed to integrate and classify the human motion target gesture data features to realize the recognition of human motion target. The results show that the proposed algorithm has high classification accuracy and overlap ratio of human motion target gesture, the recognition accuracy is as high as 93%, the recall rate is as high as 88%, and the recognition time is 17.8 s, which can effectively improve the human motion target attitude recognition effect.

Highlights

Humans’ perception of information from the outside world is mainly obtained by vision
In response to the above problems, this paper proposes a human motion target gesture recognition algorithm based on deep neural network. e idea is as follows: (1) According to the static gesture of the human, the distance between the key nodes is calculated. e Kinect interface equipment is used to collect the coordinate information of the human bone joints, calculate the difference in the feature value of the human motion gesture, extract the node characteristics of the motion gesture, and use the deep neural network to build the overall structure of the key node network and reduce the node position
This paper proposes a human motion target gesture recognition algorithm based on deep neural network and uses MSCOCO data set and MPII data set as data sources to test the proposed algorithm, which verifies the superiority of the method proposed in this paper

Summary

Introduction

Humans’ perception of information from the outside world is mainly obtained by vision. In literature [5], a deep neural network based on contextual long- and short-term memory architecture is proposed, which uses content and metadata to detect robot context features It extracts from user metadata and uses it as an auxiliary input to process the tweet text in the contextual long- and short-term memory network, but the feature extraction effect of this method is poor. In literature [6], a new method for training deep neural networks to synthesize dynamic motion primitives is proposed It can use a new loss function to measure the physical distance between motion trajectories, rather than between parameters that have no physical meaning. In response to the above problems, this paper proposes a human motion target gesture recognition algorithm based on deep neural network. (3) e depth neural network classifier is constructed to obtain the weighted value of the depth neural network classifier, calibrate the gesture features of the human motion target, fuse and classify the gesture data characteristics of the human motion target, obtain the result of the gesture recognition of the human motion target, and realize the gesture recognition of the human motion target

Related Work

Experimental Analysis and Results

Conclusions