Home environment fall detection system based on a cascaded multi-SVM classifier

Fall detection system for intelligent home care for elderly people is presented in this paper. The system includes human blob detection by non-parameter background substruction method, feature extraction from two minimum bounding boxes, and fall detection by a cascaded multi-SVM classifier. Besides falling down, other daily activities such as walk, jogging, sitting down, squatting down and immobility are also taken into consideration. A three-stage cascade of SVM classifiers is made to distinguish fall action from other activities. Each SVM classifier is first trained and tested separately to achieve its best classification performance by choosing proper features and corresponding kernel function. Then the combined classifier is trained to detect falls. A perfect correct identification rate of 98.13% on a real activity video set by experiments demonstrates the robustness and the utility of the system.