Learning and Vision-based approach for Human fall detection and classification in naturally occurring scenes using video data

Abstract

The advancement of medicine presents challenges for modern cultures, especially with unpredictable elderly falling incidents anywhere due to serious health issues. Delayed rescue for at-risk elders can be dangerous. Traditional elder safety methods like video surveillance or wearable sensors are inefficient and burdensome, wasting human resources and requiring caregivers' constant fall detection monitoring. Thus, a more effective and convenient solution is needed to ensure elderly safety. In this paper, a method is presented for detecting human falls in naturally occurring scenes using videos through a traditional Convolutional Neural Network (CNN) model, Inception-v3, VGG-19 and two versions of the You Only Look Once (YOLO) working model. The primary focus of this work is human fall detection through the utilization of deep learning models. Specifically, the YOLO approach is adopted for object detection and tracking in video scenes. By implementing YOLO, human subjects are identified, and bounding boxes are generated around them. The classification of various human activities, including fall detection is accomplished through the analysis of deformation features extracted from these bounding boxes. The traditional CNN model achieves an impressive 99.83% accuracy in human fall detection, surpassing other state-of-the-art methods. However, training time is longer compared to YOLO-v2 and YOLO-v3, but significantly shorter than Inception-v3, taking only around 10% of its total training time.