Traffic scene segmentation based on boosting over multimodal low, intermediate and high order multi-range channel features

In this paper we introduce a novel multimodal boosting based solution for semantic segmentation of traffic scenarios. Local structure and context are captured from both monocular color and depth modalities in the form of image channels. We define multiple channel types at three different levels: low, intermediate and high order channels. The low order channels are computed using a multimodal multiresolution filtering scheme and capture structure and color information from lower receptive fields. For the intermediate order channels, we employ deep convolutional channels that are able to capture more complex structures, having a larger receptive field. The high order channels are scale invariant channels that consist of spatial, geometric and semantic channels. These channels are enhanced by additional pyramidal context channels, capturing context at multiple levels. The semantic segmentation is achieved by a boosting based classification scheme over superpixels using multi-range channel features and pyramidal context features. A pre-segmentation is used to generate semantic channels as input for more powerful final segmentation. The final segmentation is refined using a superpixel-level dense conditional random field. The proposed solution is evaluated on the Cityscapes segmentation benchmark and achieves competitive results at low computational costs. It is the first boosting based solution that is able to keep up with the performance of deep learning based approaches.