Research on human fall detection technology based on improved SSD_MobileNet_v2

Ying Wang

doi:10.1117/12.2646763

7 September 2022 Research on human fall detection technology based on improved SSD_MobileNet_v2

Ying Wang

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 1232923 (2022) https://doi.org/10.1117/12.2646763
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

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%.

Citation Download Citation

Ying Wang "Research on human fall detection technology based on improved SSD_MobileNet_v2", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 1232923 (7 September 2022); https://doi.org/10.1117/12.2646763

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KEYWORDS

Data modeling

Target detection

Optimization (mathematics)

Detection and tracking algorithms

Convolution

Performance modeling

Feature extraction

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