Indoor Localisation through Probabilistic Ontologies

Abstract

For elderly people that are living alone in their homes there is a need to permanently monitor them. One of this aspect consist in knowing their indoor position and motion behavioural status, in real time. One possibility for indoor positioning of an user consists in understanding the images provided by supervising cameras. In this case the main aspect is represented by recognition of objects from these images. Thus, object recognition plays an essential part in understanding the environment and adding meaning to it. This paper presents a method for indoor localisation based on identifying the user’s context. The user’s context is computed based on object recognition and using a probabilistic ontology. The key element is represented by the probabilistic ontology that describes objects, scenes and relations between them. This ontology contains probabilistic relations that are learned using a large database. Results show that given a set of object detectors with high detection rate and low false positive rate, the system can recognize the user’s context with high accuracy.

An Indoor Localization System for Automotive Driving Competitions

“…The interest in localization of autonomous mobile systems is a highly active research topic that has been facilitated by the developments in advanced mobile and communication technologies [12,17]. Computing the position and the orientation of an object is a key technology that can bring us a step closer to autonomous vehicles, enabling them to manage in an autonomous way traffic situations and signifficantly reduce the number of accidents [7,10].…”

Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

“…Human Activity Recognition (HAR) is an emerging research area in human-machine interaction [5,16,19]. HAR is also an essential aspect of other fields, especially localization [21,26], health monitoring [28], medical diagnostics and rehabilitation [14,33]. HAR has challenges that are common with those in other recognition areas, such as face recognition and speech recognition.…”