  As the scene information, including objectness and scene type, are important
for people with visual impairment, in this work we present a multi-task
efficient perception system for the scene parsing and recognition tasks.
Building on the compact ResNet backbone, our designed network architecture has
two paths with shared parameters. In the structure, the semantic segmentation
path integrates fast attention, with the aim of harvesting long-range
contextual information in an efficient manner. Simultaneously, the scene
recognition path attains the scene type inference by passing the semantic
features into semantic-driven attention networks and combining the semantic
extracted representations with the RGB extracted representations through a
gated attention module. In the experiments, we have verified the systems'
accuracy and efficiency on both public datasets and real-world scenes. This
system runs on a wearable belt with an Intel RealSense LiDAR camera and an
Nvidia Jetson AGX Xavier processor, which can accompany visually impaired
people and provide assistive scene information in their navigation tasks.
