Human body behavior recognition method based on kernel sparse coding

A recognition method, kernel sparse technology, applied in biometrics recognition, character and pattern recognition, instruments, etc., can solve problems such as inability to effectively integrate different features, limit the improvement of recognition accuracy, and reduce the strength of feature representation, so as to improve computing The effect of speed and recognition accuracy

Active Publication Date: 2016-09-07
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0008] Human behavior recognition is affected by factors such as inter-class changes and intra-class changes of human behavior, behavior execution environment, camera position, and changes in human behavior in time and spa

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  • Human body behavior recognition method based on kernel sparse coding

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[0033] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the implementation methods and accompanying drawings.

[0034] see figure 1 , the realization of the present invention comprises the following steps:

[0035] Step S01: Input video.

[0036] Step S02: Extract the covariance feature of the input video, that is, extract the behavior feature vector f(s).

[0037] First, the input video is divided into L frames (a complete human behavior is about 0.4s ~ 0.6s, the length of L is at least set to cover the complete human behavior, usually L can be 20) and overlapping video segments. The moving step of the extracted video segment can be adjusted according to the actual situation (for example, it is set to 8 frames).

[0038] Perform feature extraction on the pixels of the video segment to obtain the behavior feature vector f(x, y, t) of the pixel...

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Abstract

The invention discloses a human body behavior recognition method based on kernel sparse coding, and belongs to the technical field of digital image processing. The method comprises the steps: firstly dividing an inputted video into video segments which have a fixed length and are mutually overlapped; secondly extracting the gradient and a light stream characteristic covariance or shape characteristic covariance of each video segment; and thirdly carrying out the dimension reduction of a covariance matrix through employing a symmetric positive definite matrix dimension reduction method. On the basis of the Stein kernel, the method proposes a sparse maximization symmetric positive definite matrix dictionary learning, and proposes a Riemann sparse solver which enables Riemann manifold to be embedded into a kernel Hilbert space. The method is used for the recognition of human body behaviors in a video, is simple in processing, is low in calculation complexity, and is robust for behavior difference, view change and low resolution.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and relates to relevant theoretical knowledge such as computer vision and pattern recognition, in particular human behavior recognition based on a covariance matrix. Background technique [0002] Human behavior recognition is a research hotspot and difficulty in the field of computer vision. Its core is to use computer vision technology to automatically detect, track, and recognize people from video sequences and understand and describe their behavior. Human motion analysis and behavior recognition methods are the core content of human behavior understanding, mainly including video human detection, tracking moving human body, obtaining relevant parameters of human behavior, and finally achieving the purpose of understanding human behavior. [0003] Human behavior recognition methods are mainly used in intelligent monitoring systems to actively and real-time analyze human behavior...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46
CPCG06V40/20G06V40/10G06V10/44
Inventor 解梅黄成挥程石磊刘伸展
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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