視覚障害者向けウェアラブルシステムにおけるリアルタイム意味セグメンテーションとシーン認識による知覚フレームワーク
Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired
視覚障害者支援のため、ResNetを基盤に意味セグメンテーションとシーン認識を同時に行う軽量な知覚システムを開発し、ウェアラブルベルト上で実時間動作を実現した。
著者: Yingzhi Zhang, Haoye Chen, Kailun Yang, Jiaming Zhang, Rainer Stiefelhagen
分類: cs.CV, cs.RO, eess.IV
原文アブストラクト
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.
