Falling behavior recognition method based on three-dimensional convolutional neural network

Abstract

The invention discloses a falling behavior recognition method based on a three-dimensional convolutional neural network, and the method comprises the steps: firstly obtaining and preprocessing a falling data set video, and obtaining a falling behavior video sample; removing the background of the video by adopting a target detection method combining a three-frame difference method based on Gaussianmixture and an adaptive threshold, and obtaining a complete human body target region by adopting a small-area removal and morphological method; extracting optical flow motion history image features of a human body target area, and adding a sample set to the feature images in a data overlapping amplification mode; randomly dividing the overlapped and amplified falling behavior sample set into a training sample set and a verification sample set according to a ratio of 7: 3, inputting the training sample set and the verification sample set into a 3D convolutional neural network model classifier,carrying out continuous iterative training, and continuously verifying the model classifier by using the verification sample set; and inputting the test sample set into the trained model classifier to complete tumble behavior identification. According to the invention, the problems of low classification recognition rate and low precision caused by background interference of the existing fall detection method are solved.

Description

technical field [0001] The invention belongs to the technical field of image classification and recognition methods, in particular to a fall behavior recognition method based on a three-dimensional convolutional neural network. Background technique [0002] With the global aging phenomenon intensifying, falls have become one of the primary health threats to the elderly. More and more elderly people live alone without anyone to take care of them. When an accident occurs, they cannot be found in time, which leads to great safety hazards in the life of the elderly. [0003] With the continuous development of various constructions such as safe cities and intelligent transportation in my country, the method of integrating machine vision technology into video surveillance systems has become a hot research issue. At present, most of the existing methods use the traditional machine learning method to recognize the fall behavior, and the recognition rate is not high, which leads to ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a fall behavior recognition method based on a three-dimensional convolutional neural network, which solves the problem of low classification recognition rate and accuracy caused by background interference in existing fall detection methods

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