An Automated Approach for Video Surveillance Using Kalman Filter

Image processing has played a vital role in every aspect of human life. Video surveillance has reached a major out through by the application of advanced image processing and frame modeling techniques. In video surveillance, detection of relocating objects from a video is predominant for object detection, target monitoring, and behavior. Detection of relocating objects in video streams is the first major step of the algorithm, and background subtraction is a pleasant technique for foreground segmentation. In this thesis, automatic actual-time object detection and tracking had been carried out utilizing Kalman filter the place the method output was once monitoring the input and canceling out any variants because of entry and exit noises. This work could be used to boost a surveillance procedure of static digicam and robotic automation visible systems