2018/11/14 by Shreya Goyal, Goyal, Shreya, Satya Bhavsar +7
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Multimedia (cs.MM) #Multimodal Machine Learning Applications #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.1812.00874
openalex publication_date 2018/11/14 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
In this paper, we propose SUGAMAN (Supervised and Unified framework using\nGrammar and Annotation Model for Access and Navigation). SUGAMAN is a Hindi\nword meaning "easy passage from one place to another". SUGAMAN synthesizes\ntextual description from a given floor plan image for the visually impaired. A\nvisually impaired person can navigate in an indoor environment using the\ntextual description generated by SUGAMAN. With the help of a text reader\nsoftware, the target user can understand the rooms within the building and\narrangement of furniture to navigate. SUGAMAN is the first framework for\ndescribing a floor plan and giving direction for obstacle-free movement within\na building. We learn 5 classes of room categories from 1355 room image\nsamples under a supervised learning paradigm. These learned annotations are fed\ninto a description synthesis framework to yield a holistic description of a\nfloor plan image. We demonstrate the performance of various supervised\nclassifiers on room learning. We also provide a comparative analysis of system\ngenerated and human written descriptions. SUGAMAN gives state of the art\nperformance on challenging, real-world floor plan images. This work can be\napplied to areas like understanding floor plans of historical monuments,\nstability analysis of buildings, and retrieval.\n