Human Motion Gesture Recognition Based on Computer Vision

Rui Ma,Zhendong Zhang,Enqing Chen,Wei Wang

doi:10.1155/2021/6679746

Abstract

Human motion gesture recognition is the most challenging research direction in the field of computer vision, and it is widely used in human-computer interaction, intelligent monitoring, virtual reality, human behaviour analysis, and other fields. This paper proposes a new type of deep convolutional generation confrontation network to recognize human motion pose. This method uses a deep convolutional stacked hourglass network to accurately extract the location of key joint points on the image. The generation and identification part of the network is designed to encode the first hierarchy (parent) and the second hierarchy (child) and show the spatial relationship of human body parts. The generator and the discriminator are designed as two parts in the network, and they are connected together in order to encode the possible relationship of appearance and, at the same time, the possibility of the existence of human body parts and the relationship between each part of the body and its parental part coding. In the image, the key nodes of the human body model and the general body posture can be identified more accurately. The method has been tested on different data sets. In most cases, the results obtained by the proposed method are better than those of other comparison methods.

Highlights

Human body gesture recognition is an important research direction of computer vision [1, 2]
It should be said that the research methods of gesture recognition cover almost all theories and technologies in the field of computer vision, such as pattern recognition, machine learning, artificial intelligence, image graphics, and statistics [3, 4]
Dong et al [5] proposed to learn the corresponding target contour model from the segmented image and used the boost classifier to find the contour of the target in the image so as to obtain the position information of each part of the human body. e literature [6, 7] uses the HOG method to extract the information of each part of the human body in the image and uses the classical algorithm support vector machine and random forest to identify and classify

Summary

Introduction

Human body gesture recognition is an important research direction of computer vision [1, 2]. Li et al [10] used the canny operator to extract edge features from the image in combination with pixel depth information and determined the head position of the person in the image through distance transformation and model matching and positioned the human body according to the prior human body proportion. Alshawabkeh [18] combined pixel depth information and used canny operator to extract edge features from the image, through the distance transformation and model matching method, to determine the head position of the person in the image and locate the human body based on the prior human body proportion. Luo et al [19] used computer graphics technology to construct a depth image database of human pose and used a classifier model to detect human body parts on a common PC. Hierarchical adversarial networks help to accurately estimate the positions of various parts of the body, especially body parts that are deformed or highly occluded

Human Motion Gesture Recognition

Improved Generative Adversarial Network Algorithm

Results

Results and Discussion

Algorithm Recognition Performance Analysis