Rate Control For People detection in video

People Detection in video is generally used in high level multimedia applications like intelligent surveillance systems, augmented reality ,etc. People detection is based on background subtraction. Generally videos are available in compressed form due to which noise will be added in it. Many algorithms are used to detect people and control their rate. There are three key steps in video analysis detection of interesting moving objects, tracking of such objects from frame to frame, and analysis of object tracks to recognize their behavior. The ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of this basic intelligent behavior still remains a challenge. In this paper we put our attention into background subtraction and Gaussian grouping of pixels for detection of people in low quality video i.e. improve accuracy.