Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges (original) (raw)
2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
Semantic segmentation was seen as a challenging computer vision problem few years ago. Due to recent advancements in deep learning, relatively accurate solutions are now possible for its use in automated driving. In this paper, the semantic segmentation problem is explored from the perspective of automated driving. Most of the current semantic segmentation algorithms are designed for generic images and do not incorporate prior structure and end goal for automated driving. First, the paper begins with a generic taxonomic survey of semantic segmentation algorithms and then discusses how it fits in the context of automated driving. Second, the particular challenges of deploying it into a safety system which needs high level of accuracy and robustness are listed. Third, different alternatives instead of using an independent semantic segmentation module are explored. Finally, an empirical evaluation of various semantic segmentation architectures was performed on CamVid dataset in terms of accuracy and speed. This paper is a preliminary shorter version of a more detailed survey which is work in progress. A. Classical methods Few years ago, semantic segmentation was seen as a challenging problem to achieve reasonable accuracy. The main approaches used in semantic segmentation was based on random forest classifier or conditional random fields. In [17] decision
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