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Robustifying semantic cognition of traversability across wearable RGB-depth cameras
- Source :
- e_Buah Biblioteca Digital Universidad de Alcalá, instname
- Publication Year :
- 2019
- Publisher :
- The Optical Society, 2019.
-
Abstract
- Semantic segmentation represents a promising means to unify different detection tasks, especially pixelwise traversability perception as the fundamental enabler in robotic vision systems aiding upper-level navigational applications. However, major research efforts are being put into earning marginal accuracy increments on semantic segmentation benchmarks, without assuring the robustness of real-time segmenters to be deployed in assistive cognition systems for the visually impaired. In this paper, we explore in a comparative study across four perception systems, including a pair of commercial smart glasses, a customized wearable prototype and two portable RGB-Depth (RGB-D) cameras that are being integrated in the next generation of navigation assistance devices. More concretely, we analyze the gap between the concepts of “accuracy” and “robustness” on the critical traversability-related semantic scene understanding. A cluster of efficient deep architectures is proposed, which are built using spatial factorizations, hierarchical dilations and pyramidal representations. Based on these architectures, this research demonstrates the augmented robustness of semantically traversable area parsing against the variations of environmental conditions in diverse RGB-D observations, and sensorial factors such as illumination, imaging quality, field of view and detectable depth range.<br />Ministerio de Economía y Competitividad<br />Comunidad de Madrid<br />Dirección General de Tráfico
- Subjects :
- Visual perception
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Wearable computer
Image processing
computer.software_genre
01 natural sciences
010309 optics
Optics
Robustness (computer science)
0103 physical sciences
Segmentation
Computer vision
Electrical and Electronic Engineering
Engineering (miscellaneous)
Parsing
business.industry
Cognition
Atomic and Molecular Physics, and Optics
RGB color model
Electrónica
Artificial intelligence
Electronics
business
computer
Subjects
Details
- ISSN :
- 21553165 and 1559128X
- Volume :
- 58
- Database :
- OpenAIRE
- Journal :
- Applied Optics
- Accession number :
- edsair.doi.dedup.....782e5f679c3c01308ea47e3849c0494e