Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifying Most Walkable Direction for Navigation in an Outdoor
  Environment   We present an approach for identifying the most walkable direction for
navigation using a hand-held camera. Our approach extracts semantically rich
contextual information from the scene using a custom encoder-decoder
architecture for semantic segmentation and models the spatial and temporal
behavior of objects in the scene using a spatio-temporal graph. The system
learns to minimize a cost function over the spatial and temporal object
attributes to identify the most walkable direction. We construct a new
annotated navigation dataset collected using a hand-held mobile camera in an
unconstrained outdoor environment, which includes challenging settings such as
highly dynamic scenes, occlusion between objects, and distortions. Our system
achieves an accuracy of 84% on predicting a safe direction. We also show that
our custom segmentation network is both fast and accurate, achieving mIOU (mean
intersection over union) scores of 81 and 44.7 on the PASCAL VOC and the PASCAL
Context datasets, respectively, while running at about 21 frames per second.

11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat