  We explore the problem of intersection classification using monocular
on-board passive vision, with the goal of classifying traffic scenes with
respect to road topology. We divide the existing approaches into two broad
categories according to the type of input data: (a) first person vision (FPV)
approaches, which use an egocentric view sequence as the intersection is
passed; and (b) third person vision (TPV) approaches, which use a single view
immediately before entering the intersection. The FPV and TPV approaches each
have advantages and disadvantages. Therefore, we aim to combine them into a
unified deep learning framework. Experimental results show that the proposed
FPV-TPV scheme outperforms previous methods and only requires minimal FPV/TPV
measurements.
