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

Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired