A fractional order variational model for tracking the motion of objects in the applications of video surveillance

Abstract

Motion tracking of moving objects from a sequence of images is an active research area devoted to the applications of video surveillance. The motion estimation is performed by means of optical flow. In this paper, a fractional order variational model is presented for motion estimation. In particular, the proposed model generalizes the existing variational models from integer order to fractional order. More specific, the fractional order derivative describes discontinuous information about texture and edges, whereas integer order unable to do so, and therefore, a more suitable in estimating the optical flow. The Grünwald-Letnikov derivative is used as a discretization scheme to discretize the complex fractional order partial differential equations corresponding to the Euler-Lagrange equations. The resulting system of equations is solved by using the conjugate gradient method. Experimental results on various datasets verify the validity of the proposed model.