Evolutive algorithms for visual fall detection in intelligent spaces

Artificial vision provides a remarkable good sensor when developing applications for intelligent spaces. Cameras are passive sensors that supply a great amount of information and are quite cheap. This paper presents an application for elderly care that detects falls or faints and automatically triggers the health alarm. It promotes the independent lifestyle of elder people at their homes as the monitoring application will call for timely health assistance when needed. The system extracts 3D information from several cameras and performs 3D tracking of the people in the intelligent space. Two evolutive multimodal algorithms have been developed to continuously estimate the 3D position in real time, one based on 3D points and another one based on 3D prisms. Both learn the visual appearance of the persons and use color and movement as tracking cues. The system has been validated with some experiments in different real environments.