Fall detection system of elderly people based on integral image and histogram of oriented gradient feature

Falls represent a major cause of fatal injury, especially for the elderly, which accordingly create a serious obstruction for their independent living. Many efforts have been put towards providing a robust method to detect falls accurately and timely. This paper proposes an alerting system for detecting falls of the elderly people that monitors seniors via detecting the elderly faces and their bodies in order to generate an alert on falling detection. The proposed system consists of three phases that are pre-processing, feature extraction, and detecting phases. The integral image-based approach for multi-scale feature extraction developed to characterize the distinctive and robust patterns of different face poses. The histogram of oriented gradient (HOG) of extracted feature is then computed. The experiments were done on the datasets which consists of 191 recorded videos annotated human images with a large range of pose variations and backgrounds. The design of the fall detection system can increase the living time and reduce the rate of death due to the fall and shows the promising performance of the proposed system.