Adaptive non-planar road detection and tracking in challenging environments using segmentation-based Markov Random Field

Many roads made for land vehicles are not totally planar and present uphill and downhill slopes that follow the environment topography. Moreover, the road appearance is often affected by a number of factors in challenging conditions. In this paper, we present an adaptive non-planar road detection and tracking approach which overcomes these difficulties by a piecewise planar road model as well as a Markov Random Field (MRF)-based alternating optimization using belief propagation (BP) on segmented images and a hard conditional Expectation Maximization (EM) algorithm to achieve adaptability and optimality. The proposed framework incorporates image evidence, geometry information, and temporal support such that the graph we build and the well-defined energy minimization formulation can exploit the essence of the roads that is invariant in challenging environments. Experimental results in various real challenging traffic scenes show the effectiveness of the proposed approach.