Multi-view and multi-state gait recognition method

Abstract

The invention provides a multi-view and multi-state gait recognition method. The method comprises the following steps of: carrying out contour extraction and time synchronization on a gait video in a training set; establishing projection relations among presentative expected value, visual angle, state and identity; carrying out visual angle estimation on a human body gait contour sequence of a prototype set so as to obtain identity information from the projection relations among the presentative expected value, the visual angle, the state and the identity; carrying out visual angle estimationon a human body gait contour sequence of a testing set so as to obtain identity information from the projection relations among the presentative expected value, the visual angle, the state and the identity in the training set; and comparing the identity information with all existing identity vectors in the prototype set so as to judge the person in the prototype set. The multi-view and multi-state gait recognition method provided by the invention is different from the traditional method based on the whole gait period that the variation process of the gait motion is more accurately modeled by using single state as minimum unit, multi-state model under each visual angle is trained and the gait visual angle of the video to be identified can be estimated.

Description

technical field

[0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a multi-view and multi-state gait recognition method. Background technique

[0002] Gait is the way a person walks. During walking, the joints of the upper and lower limbs change regularly. This change reflects the unique movement mode of the individual and can be used for effective identification. Through the analysis of gait, we can get a variety of useful information such as identity, gender, and race.

[0003] In recent decades, a large number of surveillance cameras have been installed in banks, shopping malls, subway stations, prisons and other places. These security-sensitive places urgently need an intelligent early warning method. Manual monitoring methods are no longer in line with the current security needs, because it not only consumes a lot of manpower and financial resources, but also makes it difficult to achieve the purpose of safety earl...

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