Research on human fall detection technology based on improved SSD_MobileNet_v2
Ying Wang · Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022) · 2022
Undetected elderly fall will result in serious consequences, therefore, in the informatization of elderly care services, fall detection and intelligent warning for the elderly at home is increasingly significant, which has become a hot spot of the current research. A fall detection scheme for the elderly in embedded devices by using the video stream analysis method based on the target detection model and combined with the change of human posture to determine the fall state is proposed in this paper. The clustering algorithm is used to analyze the centralized value of the aspect ratio of the dataset according to the characteristics of the single-type target detection scene in the application and the aspect ratio of the anchor in the selected SSD algorithm is reset. The method of self-adaptive judgment of positive and negative samples in China improves the identification efficiency of the elderly by using the characteristics of the data set itself. Theoretical analysis and experimental results show that the accuracy of elderly identification is increased by 5.7%, and the accuracy of fall event judgment is increased by 6.3%.