Abstract
To address problems of serious loss of details and low detection definition in the traditional human motion posture detection algorithm, a human motion posture detection algorithm using deep reinforcement learning is proposed. Firstly, the perception ability of deep learning is used to match human motion feature points to obtain human motion posture features. Secondly, normalize the human motion image, take the color histogram distribution of human motion posture as the antigen, search the region close to the motion posture in the image, and take its candidate region as the antibody. By calculating the affinity between the antigen and the antibody, the feature extraction of human motion posture is realized. Finally, using the training characteristics of deep learning network and reinforcement learning network, the change information of human motion posture is obtained, and the design of human motion posture detection algorithm is realized. The results show that when the image resolution is 384 × 256 px, the motion pose contour detection accuracy of this algorithm is 87%. When the image size is 30 MB, the recognition time of this method is only 0.8 s. When the number of iterations is 500, the capture rate of human motion posture details can reach 98.5%. This shows that the proposed algorithm can improve the definition of human motion posture contour, improve the posture detailed capture rate, reduce the loss of detail, and have better effect and performance.
Highlights
Nowadays, with the widespread of the Artificial Intelligence (AI) in various fields, surveillance system has been born, which gradually expands the advantage of deep learning in the field of visual computers and clarifies the development direction of image processing technology [1,2,3]
Starting from the field of surveillance and security, traditional surveillance technology is widely used in the military field, such as customs defense and borders. e detection and tracking technology based on deep reinforcement learning can be used to assist manual completion of designated tasks [6, 7]. e application of deep reinforcement learning human motion posture detection to sports items can provide services for sports training viewing [8]
In view of the serious loss of details and low detection clarity of the above methods, this paper proposes a human motion posture detection algorithm based on deep reinforcement learning. e perception ability of deep learning is used to match the feature points of human motion, and by locating feature points of human motion posture, the position and direction of the human motion posture feature are determined, and the human motion posture feature is obtained. is method analyzes the contour of the human body
Summary
Received 12 September 2021; Revised October 2021; Accepted 6 December 2021; Published December 2021. To address problems of serious loss of details and low detection definition in the traditional human motion posture detection algorithm, a human motion posture detection algorithm using deep reinforcement learning is proposed. By calculating the affinity between the antigen and the antibody, the feature extraction of human motion posture is realized. Using the training characteristics of deep learning network and reinforcement learning network, the change information of human motion posture is obtained, and the design of human motion posture detection algorithm is realized. When the number of iterations is 500, the capture rate of human motion posture details can reach 98.5%. Is shows that the proposed algorithm can improve the definition of human motion posture contour, improve the posture detailed capture rate, reduce the loss of detail, and have better effect and performance
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