Intelligent Vehicle Tracking For Detection of Objects

This paper introduces the real time implementation of a vehicle in the changing environment, changes in the environment is notified through video motion transmitter and receiver. The receiver takes snapshot of an image to detect threatening objects in the environment. Those images are trained and tested by using Associative memory. The runtime image has been trained, tested and quickly retrieved for fast decision making. This robot detects threatening objects in the environment with HAM network. After making decision the remote control access is done through Zigbee device to monitor and control the speed of a Roving robot in an accurate path. Supervised learning method has been applied to detect the object correctly. Delta learning rule is applied in HAM network to test and train the image.