Deterministic learning theory based gait recognition method irrelevant to visual angle

A technology for determining learning theory and gait recognition, applied in the field of gait recognition independent of perspective, and can solve problems such as insufficient robustness to perspective changes

Inactive Publication Date: 2014-11-05
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned existing methods that are not robust to changes in viewing angles, and provide a mor

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  • Deterministic learning theory based gait recognition method irrelevant to visual angle
  • Deterministic learning theory based gait recognition method irrelevant to visual angle
  • Deterministic learning theory based gait recognition method irrelevant to visual angle

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Embodiment

[0065] Such as figure 1 As shown, a view-independent gait recognition method based on deterministic learning theory, including the following steps:

[0066] Step 1: The preprocessing process is as follows:

[0067] The gait database used in the present invention is the CASIA Dataset B database of the Institute of Automation, Chinese Academy of Sciences. The database contains a total of 124 people, each with 11 different viewing angles (0°, 18°, 36°, 54°, 72°, 90°, 108°, 126°, 144°, 162°, 180°) , 6 sequences of normal walking are taken for each viewing angle, a total of 124×11×6=8184 sequences, three of which are selected as training patterns, and the remaining three sequences are used as test patterns. The original video size is 320×240 pixels, and the sampling frequency is 30Hz. The database has been separated from the background, and the work to be done in the present invention is to perform preprocessing on this basis, thereby performing gait cycle detection and extracti...

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Abstract

The invention discloses a deterministic learning theory based gait recognition method irrelevant to a visual angle, and belongs to the technical field of pattern recognition. The method comprises the following steps: preprocessing; extracting characteristics; on the basis of extracted gait characteristics, dynamically carrying out neural network modeling and identification to a gait system at different visual angles in a training set; establishing a literal neural network; and constructing a dynamic estimator, and realizing the accurate classification recognition of test modes according to a least error principle by utilizing differences among gait modes on gait system dynamics at different visual angles. The dynamic local accurate modeling and identification of the human body gait system at different visual angles can be realized, meanwhile, gait modes at different visual angles are formed into a uniform training gait mode library, a problem of a traditional method that corresponding training sets need to be independently constructed for gait modes at different visual angles can be overcome, the gait recognition irrelevant to the visual angle is realized, and the invention exhibits higher robustness and practicality.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a gait recognition method based on deterministic learning theory and independent of viewing angle. Background technique [0002] Human gait recognition, as an emerging biometric technology, aims to find and extract the characteristics of individual changes from the same walking behavior, so as to realize automatic identification. The joint angle changes of human walking motion contain rich individual identification information, and human walking motion largely depends on the shape change of body contour over time. This change reflects the unique movement pattern of the individual, which can be effectively identified. Through the analysis of gait, we can get a variety of useful information such as identity, gender, race and so on. In recent years, as the security situation in public places has become more and more serious, a large number of surveillance c...

Claims

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Application Information

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IPC IPC(8): G06K9/66G06K9/46
Inventor 曾玮王聪
Owner SOUTH CHINA UNIV OF TECH
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