Spatiotemporal road scene reconstruction using superpixel-based Markov random field

Scene reconstruction based on image rendering is an indispensable but challenging task in computer vision and intelligent transportation systems. We propose a framework for reconstructing road scenes as 3D corridor models and consisting of two stages: road detection and scene reconstruction. Road detection is realized by the novel superpixel-based Markov random field. The data fidelity term in the energy function is jointly computed according to superpixel features of color, texture and location. The smoothness term is established from the interaction of spatiotemporally adjacent superpixels. In the subsequent scene reconstruction, the foreground and background regions are modeled independently. Experiments on road detection demonstrate that our proposal outperforms state-of-the-art methods in both accuracy and computation speed. Scene reconstruction experiments further confirm that the scene models retrieved by the proposed method have higher correctness ratio, and can support several applications.