Authors : O. Dorgham, Sanad Abu Rass and Habes Alkhraisat
Abstract: Statistical studies show that around 28-35% of older people aged 65 and over fall each year. This percentage increases to 32-42% among those over 70 years of age. These figures explain the dramatic increase in the number of systems that have been developed in recent years with aim of detecting falls. In this study, we propose, implement and evaluate a multiphase system framework to analyze human motion in real time to detect falls among the elderly. The system phases consist of background subtraction to extract the foreground of the frame for further analysis object classification which performs some morphological operations and draws the contours of the detected objects to identify human bodies object tracking to reduce false alarms and fall detection to detect the occurrence of falls based on the bounding rectangle and surrounding points contour drawing methods and by utilizing dual-camera verification as well as a method of leg detection using a camera situated above the subject. The system starts with a background subtraction phase to detect moving objects. After that the moving object is classified as a human body or not. Objects classified as human bodies are then tracked to detect falls. The experimental results showed that the system has high performance and accuracy and that it can implement and process live videos and report falls instantly. Our system can process videos at an approximate frame rate of 20 FPS (using an Intel 2.8 GH quad core processor with 4 GB RAM) and with an accuracy of 88.1%.
O. Dorgham, Sanad Abu Rass and Habes Alkhraisat, 2017. Improved Elderly Fall Detection by Surveillance Video using Real-Time Human Motion Analysis. International Journal of Soft Computing, 12: 253-262.