Video-based Fall Detection Research And Application

With the advent of China's aging society,the number of elderly people living alone has gradually increased.Accidental falls are becoming the first factor in the casualties of elderly living alone.So,it is necessary to monitor the daily behavior of the elderly living alone.When older people fall,the system can quickly and accurately detect the falling behavior and timely report to the police,reducing the elder casualties rate.This paper analyzes domestic and foreign fall detection methods,based on computer vision and image processing technology,builds an intelligent real-time monitoring system on an embedded platform.The main research work of this paper is as follows:In the moving human target extraction stage,the foreground of the moving human target is extracted by using the Gaussian mixture model based on the background subtraction method,and the fusion background subtraction and edge extraction method is proposed;the method combining the Sobel operator and the Laplacian operator is proposed,it has effectively improved the accuracy of foreground extraction;combine color information to suppress the shadows generated by the image;use morphology,filtering and other methods to denoise the image.In the fall feature extraction stage,the human body's fall posture characteristics and the evaluation criteria that need to be considered when extracting fall behavior features are analyzed.The smallest bounding rectangular box is used to mark the moving human body;the human body target falls feature extraction method is designed and will be extracted.These features are combined into a vector to complete the conversion of the image to the numerical space.In the stage of fall behavior recognition,a fall detection method based on multifeature fusion is proposed.This method can recognize the falling behavior of the elder.In order to improve the accuracy of fall recognition,introducing support vector machine;This article standardizes the extracted fall features,the SVM kernel function and its parameters are determined,the model training is completed,and the classifier is obtained.This paper proposes a fall detection algorithm based on multi-feature fusion and SVM,and conducts a comparative analysis of algorithm experiments.The experiment proves that the fall detection algorithm proposed in this paper can effectively reduce the rate of misjudgment and improve the accuracy of fall detection.This article uses the Raspberry Pi as the core hardware,configuring the environment of the fall detection algorithm on the Raspberry Pi,builds an intelligent real-time monitoring system,completing real-time fall detection and alarm functions.