Human Fall Detection Algorithm Based on YOLOv3
Xiang Wang, Kebin Jia
Abstract
With the increase of the elderly population, the phenomenon of the elderly falling at home or out is more and more common. Therefore, fall detection is of great significance for the health protection of the elderly. Throughout the research of fall detection at home and abroad, most of the fall detection based on video monitoring is complex and redundant, which affects the real-time and accuracy of detection. In view of the above problems, this paper proposes a fall detection method based on video in complex environment, aiming to detect fall behavior more accurately and quickly. The main work of this paper is as follows: firstly, YOLOv3 network model is proposed for detection algorithm. Secondly, the human fall detection data set is constructed by referring to Pascal VOC data set format. Then, the algorithm model is optimized and trained in GPU (graphic processing unit) deep learning server. Finally, comparison of test results with our YOLOv3 network model and other detection algorithms shows that our detection algorithm has a good recognition effect.