Understanding of wheelchair ramp scenes for disabled people with visual impairments. (April 2020)

• Record Type: Journal Article
• Authors: Wang, Luping; Wei, Hui
• Is Part Of: Engineering applications of artificial intelligence. Volume 90(2020)
• Publication Date: 2020-04
• Subjects: Wheelchair ramp scene -- Non-manhattan structure -- Monocular vision -- Disabled people -- Visual impairments; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2020.103569
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Helping disabled people with visual impairments understand wheelchair ramp scenes has considerable value in computer vision. However, due to the diversity of wheelchair ramp scenes, understanding them remains a big challenge. Wheelchair ramp planes can be considered as a composition of rectangles that do not satisfy manhattan assumption. These non-manhattan rectangles are projected into two dimensional projections, shaping into special geometric configurations, which may enable us to estimate their original orientation and position in 3D scenes. In this paper, we presented a method for disabled people with visual impairments to understand wheelchair ramp scenes from a single image without any prior training. Firstly angle projections can be assigned to different clusters. Secondly ramp vanishing points (RVPs) can be estimated. Then it is possible to determine ramp planes consisting of angle projections that belong to the estimated RVPs. Finally, the algorithm can understand wheelchair ramp scenes including not only manhattan structures but also ramp planes belonging to non-manhattan structures. The proposed approach requires no prior training or any knowledge of the camera's internal parameters. Besides, it is robust to the errors in calibration and image noise. We compared the estimated wheelchair ramp scene layout against the ground truth, measuring the percentage of pixels that were incorrectly classified. The experimental results showed that the method can understand wheelchair ramp scenes including not only manhattan structures but also non-manhattan structures of ramp planes, making it practical and efficient for disabled people with visual impairments.