Human behavior identification method based on non-negative matrix decomposition and hidden Markov model

Abstract

A human behavior identification method based on non-negative matrix decomposition and a hidden Markov model comprises an off-line training stage of firstly pre-processing image of each kind of selected behavior sequence training data to obtain a total sample data matrix A of all training data, carrying out non-negative matrix decomposition (NMF) on the A to obtain a basic matrix W and a basic vector number r, and obtaining a characteristic matrix Ei of each kind of training behavior sequence according to the W and the A, and initializing the hidden Markov model (HMM) of each kind of training behavior sequence and respectively estimating an optimal parameter thereof; and an on-line identification stage of firstly pre-processing the image of the input behavior sequence to be identified to obtain an original matrix a of the behavior sequence, obtaining a characteristic matrix e according to the W and the a; and lastly, figuring up a likelihood of the behavior sequence to be identified and each kind of training behavior sequence to determine behavior types. In the invention, the human behavior identification rate is higher, and the automatic analysis ability of the human behavior applied to a real-time intelligent video monitoring system is improved.

Description

technical field [0001] The invention belongs to the field of pattern recognition, relates to the fields of artificial intelligence, computer vision and image processing, and in particular to a method for recognizing human body behavior in an intelligent video monitoring system. Background technique [0002] In recent years, video surveillance technology has attracted the attention of the society and has been applied to all aspects of life. Cameras are ubiquitous in many communities, streets, and campuses. Human behavior recognition and understanding has always been the most active topic in the field of intelligent video surveillance systems, which requires detection and tracking of moving targets from data captured by cameras, and finally recognition and understanding of target behavior. [0003] At present, the methods of human behavior recognition are mainly divided into two categories: template matching method and state space method. The former is to convert the video s...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that the feature extraction method currently used in human behavior recognition cannot extract excellent features, resulting in a low recognition rate, the present invention proposes a method based on non-negative matrix decomposition and hidden Markov model that can effectively improve the recognition rate. Human behavior recognition method, using non-negative matrix decomposition method to extract human behavior features, and using hidden Markov model to identify and classify behavior features

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