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We explore the problem of intersection classification using monocularon-board passive vision, with the goal of classifying traffic scenes withrespect to road topology. We divide the existing approaches into two broadcategories according to the type of input data: (a) first person vision (FPV)approaches, which use an egocentric view sequence as the intersection ispassed; and (b) third person vision (TPV) approaches, which use a single viewimmediately before entering the intersection. The FPV and TPV approaches eachhave advantages and disadvantages. Therefore, we aim to combine them into aunified deep learning framework. Experimental results show that the proposedFPV-TPV scheme outperforms previous methods and only requires minimal FPV/TPVmeasurements.

Use of First and Third Person Views for Deep Intersection Classification