Paper Title
Modeling Of Moving Object Detection Using GCM Alert System

In previous model image stored in the server it takes time to retrieve after detecting by estimating the absolute difference between incoming video frame and background model. In addition to this thesis, we present an operational computer vision for real-time observing, detection and tracking of human motion in a tough area. To efficiently observe such a wide area at less-cost, mobile robots are attractive options. The moving object is identified using the Cauchy distribution model. Using threshold value the detected pixel is identified. The movement of the object is identified exactly. After motion detection it will send a GCM alert to the android mobile application. Experimental results show that the algorithm is very effective and provides rapid comparison between pixel of current frame and ability to minimize both false and missed detection.