Mixture of Gaussians Exploiting Histograms of Oriented Gradients for Background Subtraction

Visual surveillance systems include a wide range of related areas ranging from motion detection, moving object classification and tracking to activity understanding. Typical applications include traffic surveillance, CCTV security systems, road sign detection. Each of the above-mentioned applications relies greatly on proper motion segmentation method. Many background subtraction algorithms have been proposed. Simple yet robust frame differencing, statistically based Mixture of Gaussians or sophisticated methods based on wavelets or the optical flow computed by the finite element method. In this paper we focus on novel modification of well known MoG. The intrinsic motivation stems from the inability of regular MoG implementation to handle many camera related phenomena. Here presented method exploits Histograms of Oriented Gradients to significantly reduce the influence of camera jitter, automatic iris adjustment or exposure control causing severe degradation of foreground mask. The robustness of introduced method is shown on series of video sequences exhibiting mentioned phenomena.