Development of an IR Video Surveillance System Based on Fractional Order TV-Model

Due to the wide range of applications, video surveillance is known as one of the challenging tasks of computer vision which requires detecting and tracking the moving objects in a sequence of images (video). As we are aware that several environmental conditions such as fog, darkness, snow-fall, illumination, rain degrade the quality of vision system. This motivates us to develop a robust infrared (IR) surveillance system to fulfill the open-ended goals of the vision problem. The active motion region is detected by using optical flow. In this paper, an energy functional has been presented for optical flow estimation by incorporating the fractional order total variational (TV) and quadratic terms. In particular, the proposed model is convex and more robust against outliers and provides a dense flow. However, the total variation regularization term is of non-differentiable nature which makes the minimization scheme apparently difficult. The fractional derivative discretization of non-differentiable terms is performed by using Grunwald-Letnikov (GL) derivative. The Primal-dual algorithm is applied in solving the resulting minimization scheme. Finally, the resulting variational formulation is solved by using an appropriate method. The validity, efficiency, and robustness of the proposed system are tested on a variety of datasets under various conditions.