Video Surveillance is an omnipresent topic when it comes to enhancing security and safety in the intelligent home environment. Artificial vision provides a remarkable good sensor. Cameras are passive sensors that supply a great amount of information.In this project we develop an application for elderly care that detects falls or faints and automatically triggers the health alarm. In this work, we propose a human-shape-based falling detection algorithm and implement this algorithm in a multi-camera video surveillance system. This algorithm uses multiple cameras to fetch the images from different regions required to monitor. It then uses a falling-pattern recognition approach to determine if an accidental falling has ccurred. If yes, the system will trigger the health alarm. It should not reset within few seconds system automatically sends short messages to someone needs to alert. Furthermore, we use the speed of fall to differentiate real fall incident and an event where the person is simply lying without falling.