Video Object Tracking based on Automatic Background Segmentation and updating using RBF neural network

In this paper, the problems associated with the automatic object segmentation of the video sequences are considered. Towards this objective, a unique method that combines of image and video processing techniques ranged from noise filtering to data clustering is developed. The method also addresses a number of challengin g issues along with computational complexity, accuracy, generality, and robustness. One of the primary aims of this paper is to find segmentation of color, texture, motion, shape, frame difference, and other methods of video segmentation for automatic dete ction considering the real - time processing requirements. In contrast to frame - wise tracking techniques, the employment of a spatiotemporal data that is constructed from multiple video frames introduces new degrees of freedom that can be exploited in terms of object extraction and content analysis. The current notions of region segmentation are extended to the spatiotemporal domain, and new models to estimate the object motion are derived