Real-Time Fall Detection Using Uncalibrated Fisheye Cameras

In this article, we describe an approach for the problem of fall detection among the several other everyday activities in indoor environment, using three uncalibrated fisheye cameras. The proposed methodology requires the input segmented silhouettes from the three simultaneously acquired frames, and it is capable of detecting fall events in every location of the imaged environment. The presented algorithm uses the model of fisheye image formation that is based on the spherical projection followed by central projection. Under this model, vertical structures are imaged as straight lines passing through the center of field of view, by a camera with approximately vertical optical axis. The main advantages of this article are the simplicity of the detecting rule, the speed of execution, and the utilization of heterogeneous omnidirectional cameras that allows simultaneous imaging along any direction. The proposed algorithm is designed and parameterized using an extensive data set of synthetic frames. The results from the real videos are presented using the frame statistics and the event-based statistics. The proposed algorithm correctly detects the fall events within standing or walking, as well as other nonfalling activities.