Fall Behavior Recognition in Home Environment Based on YOLOv8
For the algorithm problems of low accuracy and poor real-time performance of existing fall behavior recognition algorithms in the complex home environment conditions,this paper proposes a fall behavior recognition method in home environment based on YOLOv8.This method obtains video images from webcams,uses object detection algorithm based on YOLOv8 to identify the human body and fall features in each frame of surveillance video,and then combines the processing of sequential state features to set rules to identify fall behaviors and conduct fall warning.The experimental results show that the precision rate of the improved method is 94.9%,the recall rate is 95.7%,and the FPS is 40.The algorithm has high recognition accuracy and strong real-time performance,which provides a simple and effective method for fall behavior recognition.
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