Gait cycle detection method based on regional characteristics analysis

Abstract

The invention provides a gait cycle detection method based on regional characteristics analysis and the method includes the acquisition of the objective outline of a pedestrian and detection of gait cycle. The acquisition method of the objective outline includes the following steps of: first extracting a single frame image for grey scale transformation from a video, then calculating the median ofall pixel points frame by frame as a background image of the whole sequence, and finally adopting a background subtraction to extract a human objective, using mathematical morphology to fill the cavity of a binary image, analyzing and extracting the profile of human by single connection, placing the human body in the middle and unifying the dimensions of images to be 64*64pixel; and the gait cycledetection is to convert the gait cycle analysis problem to an image regional characteristics analysis problem with a single frame, namely analyzing the gait cycle according to the characteristic change situation of image regions in each frame. The method not only has small calculation quantity, but also already reaches the precision to which people subjectively judge the gait cycle, and avails the possibility of real-time gait identification.

Description

(1) Technical field [0001] The invention relates to a pattern recognition technology, in particular to a gait cycle detection method in gait recognition. (2) Background technology [0002] The purpose of gait recognition is to identify people based on their walking posture. Gait recognition can obtain the gait characteristics without the research object being aware of it. It has the advantages of non-invasiveness, non-contact, low requirement for system resolution, long-distance, difficult to camouflage, and little environmental impact. Therefore, from the viewpoint of video surveillance, gait is the most potential biometric feature in long-distance situations. Gait recognition has broad application prospects and economic value in the fields of access control systems, security monitoring, human-computer interaction, medical diagnosis, etc., so it has stimulated the enthusiasm of researchers at home and abroad. [0003] The gait sequence image is a periodic spatio-temporal ...

AI Technical Summary

Problems solved by technology

Many researchers at home and abroad have done research on gait cycle detection. BenAbdelkader et al. determined the gait cycle by calculating the self-similarity of human body contours; BenAbdelkader et al. also analyzed the periodicity of gait according to the width of the bounding rectangle; Collins et al. analyzed the periodic changes in the height and width of the human body, and then observed the gait cycle; Kale et al. analyzed the periodic characteristics of the gait by observing the norm of the human body width vector over time; Boulgouris et al. used the foreground pixel and the autocorrelation to judge the cycle of gait; Sarkar et al. used the periodic characteristics of the number of pixels in the lower half of the human body area to determine the periodic change of gait; Li et al. arranged the gait into a self-similar map (SSP), Then, the linear local embedding (LLE) non-linear dimensionality reduction method is used to extract one-dimensional features that retain the original geometric shape to analyze the periodicity of gait; Chen Shi et al. take all pedestrian contour areas in the gait sequence as a rectangular frame In the image area, within the 1 / 4 height of the image area from bottom to top, three areas are equally divided horizontally, the cumulative contour points of each area are calculated, and the corresponding point distribution histogram features are obtained to detect the gait cycle; these methods are commonly used to calculate Disadvantages of large quantity

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