6,095 research outputs found
AmIE: An Ambient Intelligent Environment for Assisted Living
In the modern world of technology Internet-of-things (IoT) systems strives to
provide an extensive interconnected and automated solutions for almost every
life aspect. This paper proposes an IoT context-aware system to present an
Ambient Intelligence (AmI) environment; such as an apartment, house, or a
building; to assist blind, visually-impaired, and elderly people. The proposed
system aims at providing an easy-to-utilize voice-controlled system to locate,
navigate and assist users indoors. The main purpose of the system is to provide
indoor positioning, assisted navigation, outside weather information, room
temperature, people availability, phone calls and emergency evacuation when
needed. The system enhances the user's awareness of the surrounding environment
by feeding them with relevant information through a wearable device to assist
them. In addition, the system is voice-controlled in both English and Arabic
languages and the information are displayed as audio messages in both
languages. The system design, implementation, and evaluation consider the
constraints in common types of premises in Kuwait and in challenges, such as
the training needed by the users. This paper presents cost-effective
implementation options by the adoption of a Raspberry Pi microcomputer,
Bluetooth Low Energy devices and an Android smart watch.Comment: 6 pages, 8 figures, 1 tabl
Fireground location understanding by semantic linking of visual objects and building information models
This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding
Vision-Based Localization Algorithm Based on Landmark Matching, Triangulation, Reconstruction, and Comparison
Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRC) global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
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