Fall Behavior Recognition Method Based on 3D Convolutional Neural Network

Abstract

The invention discloses a fall behavior recognition method based on a three-dimensional convolutional neural network. Firstly, a fall data set video is obtained and preprocessed to obtain a fall behavior video sample; the video is combined with a three-frame difference method based on a mixed Gaussian and an adaptive threshold. The target detection method removes the background, and then uses small area removal and morphological methods to obtain the complete human target area; extracts the optical flow movement history image features of the human target area, and then increases the sample set by means of data overlapping and amplification for the feature image; The falling behavior sample set after overlapping and amplifying is randomly divided into a training sample set and a verification sample set according to a ratio of 7:3 to input a 3D convolutional neural network model classifier and continuously iteratively train, while using the verification sample set to continuously verify the model classifier; The test sample set is input into the trained model classifier to complete the fall behavior recognition. The invention solves the problem of low classification recognition rate and precision caused by background interference in the existing fall detection method.

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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