Moving Objects Tracking Using Statistical Models
Abstract
Object detection plays an important role in successfulness of a wide range of applications that involve images as input data. In this paper we have presented a new approach for background modeling by nonconsecutive frames differencing. Direction and velocity of moving objects have been extracted in order to get an appropriate sequence of frames to perform frame subtraction. Stationary parts of background are extracted from differenced frames and joined as patches to complete the background model. There is also a special stage to handle changing regions of dynamic scenes. During the detection phase, the modeled background is updated for every new frame. Since it's not necessary to estimate each pixel gray value like the most common statistical methods, modeling process is not timeconsuming. Different experiments show successful results even for challenging phenomena like environmental changes.