Computation scalable disparity estimation for delay sensitive 3D video surveillance system

Disparity estimation is an important task in many 3D video surveillance applications. How to generate the disparity information at the front end under limited delay budget is very challenging in a real-time surveillance system. In this paper, we tackle this problem through adopting multiresolution strategy in the disparity estimation process. Our contribution is twofold. First, unlike existing coarse-to-fine strategies based on uniform sampling, we present a fast disparity estimation algorithm based on nonuniform sampling at the coarse level. The disparity values of the non-samples are initially interpolated through Delaunay Triangulation, and then are refined through bilateral filtering. The content-aware nonuniform sampling provides better disparity approximation in the triangulated interpolation, and consequently leads to faster convergence in the refinement procedure. Second, we model the computation time in the disparity estimation process for execution assistance in the delay sensitive surveillance system. Both the sampling cell size and the data resolution are considered in this model, in order to accommodate different runtime requirements. Experimental results demonstrate the efficiency of the proposed scheme.