Fast traffic scene segmentation using multi-range features from multi-resolution filtered and spatial context channels

In this paper we describe a fast solution for the semantic segmentation of traffic scenarios. We propose a multiresolution filtering scheme over LUV + HOG image channels using high pass and low pass filtered channels at multiple scales. To add spatial context, we extend the filtered channels with horizontal and vertical position channels. We introduce multi-range classification features that capture local structure and context for achieving fast semantic segmentation of traffic scenarios. Binary boosting based pixel classifiers are trained for each semantic class. Finally, we use these classifiers to provide the unary potential term in a dense Conditional Random Field. We evaluate the proposed solution on the CamVid traffic scene segmentation benchmark and achieve state of art results at 25 FPS, being the fastest top performing approach.