  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.
