  Street scene understanding is an essential task for autonomous driving. One
important step towards this direction is scene labeling, which annotates each
pixel in the images with a correct class label. Although many approaches have
been developed, there are still some weak points. Firstly, many methods are
based on the hand-crafted features whose image representation ability is
limited. Secondly, they can not label foreground objects accurately due to the
dataset bias. Thirdly, in the refinement stage, the traditional Markov Random
Filed (MRF) inference is prone to over smoothness. For improving the above
problems, this paper proposes a joint method of priori convolutional neural
networks at superpixel level (called as ``priori s-CNNs'') and soft restricted
context transfer. Our contributions are threefold: (1) A priori s-CNNs model
that learns priori location information at superpixel level is proposed to
describe various objects discriminatingly; (2) A hierarchical data augmentation
method is presented to alleviate dataset bias in the priori s-CNNs training
stage, which improves foreground objects labeling significantly; (3) A soft
restricted MRF energy function is defined to improve the priori s-CNNs model's
labeling performance and reduce the over smoothness at the same time. The
proposed approach is verified on CamVid dataset (11 classes) and SIFT Flow
Street dataset (16 classes) and achieves competitive performance.
