Human body behavior recognition method based on kernel sparse coding

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...

AI Technical Summary

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 space, which greatly limits the improvement of recognition accuracy.

Behavior representation often cannot effectively integrate different features, reduce the strength of feature representation, and reduce external interference

Images