Fast road scene segmentation using deep learning and scene-based models

Pixel-labeling approaches using semantic segmentation play an important role in road scene understanding. In recent years, deep learning approaches such as the deconvolutional neural network have been used for semantic segmentation, obtaining state-of-the-art results. However, the segmentation results have limited object delineation. In this paper, we adopt the de-convolutional neural network to perform the semantic segmentation of the road scene using colour and depth information. Moreover, we improve the network's limited object delineation within a computationally efficient framework using novel features that are learnt at the pixel-level and patch-level for different road scenes. The patch-level features represent the road scene geometry. On the other hand, the learnt pixel-level features represent the appearance and depth information. The features learnt for the different road scenes are indexed with the scene's pre-defined label. Following the indexing, the random forest classifier is trained to retrieve the relevant geometric and appearance-depth features for a given road scene. The retrieved features are then used to refine identified error regions in the initial semantic segmentation estimate. Our proposed algorithm is evaluated on an acquired dataset and compared with state-of-the-art baseline algorithms. We also perform a detailed parametric evaluation of our proposed framework. The experimental results show that our proposed algorithm reports better accuracy.