Human body behavior recognition based on logarithmic Euclidean space BOW (bag of words) model

Abstract

The invention discloses human body behavior recognition based on a logarithmic Euclidean space BOW (bag of words) model, and belongs to the technical field of digital image processing. The recognition comprises the steps: firstly enabling an input video to be divided into video segments which have a fixed length and are overlapped; secondly cutting each video segment into space-time cubic blocks which have the fixed size and are partly overlapped; thirdly extracting a gradient and a light stream feature covariance or a shape feature covariance of each space-time cubic block, and carrying out the dimension reduction of a covariance matrix through employing a symmetric positive definite matrix dimension reduction method. The method carries out the logarithmic change of the covariance matrix, extracts the triangular features of a logarithmic covariance matrix, and converts the triangular features into a logarithmic Euclidean space vector. The method carries out the behavior modeling for the logarithmic Euclidean space through employing the BOW model, carries out the clustering of behavior characteristics through employing spectrum clustering to generate a codebook, and codes the behavior characteristics through employing the LLC (Locality-constrained Linear Coding) technology. A nonlinear support vector machine is used for the training, recognition and classification of the behavior characteristics. The method is used for the recognition of human body behaviors, and is great in robustness.

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, especially human body behavior recognition based on a logarithmic Euclidean bag-of-words model. 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 algorithms 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...

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

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