Confidence-based Fall Detection Using Multiple Surveillance Cameras

Dara Ros, Rui Dai

Abstract: The major cause of serious or even fatal injury for the elderly is a fall. Among various technologies developed for detecting falls, the camera-based approach provides a non-invasive and reliable solution for fall detection. This paper introduces a confidence-based fall detection system using multiple surveillance cameras. First, a model for predicting the confidence of fall detection on a single camera is constructed using a set of simple yet useful features. Then, the detection results from multiple cameras are fused based on their confidence levels. The proposed confidence prediction model can be easily implemented and integrated with single-camera fall detectors, and the proposed system improves the accuracy of fall detection through effective data fusion.