Abstract
With the rapid development of sports science, human motion recognition technology, as a new biometric recognition technology, has many advantages, such as noncontact target, long recognition distance, secret recognition process, and so on. Traditional human motion recognition technology is affected by environmental factors such as motion background, which is prone to rough edges of the recognized objects and loss of motion tracking information, thus further reducing the recognition accuracy. In this paper, the traditional snake model will be improved and optimized to improve the defect of human motion model contour extraction, so as to realize the accurate repair of image contour; in terms of algorithm running time, this paper innovatively improves the construction process of the snake model, further improves the running time of model evaluation, and solves the concave contour problem of corresponding moving objects in the snake model. In order to solve the problem of accurate convergence, this paper improves the snake model of the average moving algorithm and sets the corresponding weight coefficient to distinguish the corresponding moving target background, so as to achieve the convergence of the differential concave contour. In order to verify the superiority of the improved optimized snake model, experiments are carried out in the corresponding database. The experimental results show that the contour of the moving object extracted by the improved snake model algorithm is complete and the segmentation effect is obvious. At the same time, the running speed of the whole algorithm has been significantly improved.
Highlights
Complex environmental factors in moving background, such as holes and noise, will have a negative impact on human motion [7]. e current conventional background segmentation technologies, such as background reduction algorithm, inter-frame difference algorithm, motion field algorithm based on human motion, etc., all have serious convergence and background extraction defects [8, 9]. erefore, how to extract
E structure of this paper is as follows: Section 2 of this paper will focus on the analysis of the improved snake model, focusing on the analysis of its defects in the gap, running time, and image segmentation level; Section 3 of this paper will be based on the improved snake algorithm in a large database to test and evaluate the experimental results; the article is summarized
In order to solve the problems of contour limitation and concave contour defects in the above classical models, based on the basic principle of optical flow field and using the variational algorithm in mathematics to convert the dense vector of the target moving image, the gradient vector flow of the target image is formed, so as to realize the accurate repair of the contour of the moving target image
Summary
With the continuous development of computer technology, Internet of things technology, and biometrics technology, human motion tracking and recognition technology based on related advanced algorithms has been paid more and more attention [1, 2]. e classical human motion tracking and recognition technology is mainly to separate the human object from the corresponding background area, so as to realize the classification, accurate feature extraction, accurate expression, and final recognition of human motion object from the corresponding background [3, 4]. e traditional human motion recognition algorithm is limited to the segmentation of the existing background or image, which is mainly affected by the background disturbance signal. e corresponding background edge of the segmented moving object is often rough or cannot be closed, which will cause the loss of the corresponding motion features, which seriously affects the subsequent effective processing of the algorithm. us, the efficiency of the whole algorithm is further reduced [5, 6]. E corresponding background edge of the segmented moving object is often rough or cannot be closed, which will cause the loss of the corresponding motion features, which seriously affects the subsequent effective processing of the algorithm. E conventional gait recognition technology mainly depends on the effective analysis of the moving target image sequence, which can analyze and study the moving target in detail at three levels, namely, moving target segmentation technology, feature extraction technology, and corresponding recognition and classification technology [10,11,12]. In order to solve the above problems, this paper will improve and optimize the traditional snake model [16] and improve the defect of extracting human motion model contour, so as to realize the accurate repair of image contour; in terms of algorithm running time, this paper improves the construction process of the snake model, so as to further improve the running time of model evaluation. E structure of this paper is as follows: Section 2 of this paper will focus on the analysis of the improved snake model, focusing on the analysis of its defects in the gap, running time, and image segmentation level; Section 3 of this paper will be based on the improved snake algorithm in a large database to test and evaluate the experimental results; the article is summarized
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