Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vision-Based Road Detection using Contextual Blocks   Road detection is a fundamental task in autonomous navigation systems. In
this paper, we consider the case of monocular road detection, where images are
segmented into road and non-road regions. Our starting point is the well-known
machine learning approach, in which a classifier is trained to distinguish road
and non-road regions based on hand-labeled images. We proceed by introducing
the use of "contextual blocks" as an efficient way of providing contextual
information to the classifier. Overall, the proposed methodology, including its
image feature selection and classifier, was conceived with computational cost
in mind, leaving room for optimized implementations. Regarding experiments, we
perform a sensible evaluation of each phase and feature subset that composes
our system. The results show a great benefit from using contextual blocks and
demonstrate their computational efficiency. Finally, we submit our results to
the KITTI road detection benchmark achieving scores comparable with state of
the art methods.

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The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn