Indoor Localisation Through Object Detection on Real-Time Video Implementing a Single Wearable Camera

Abstract

This paper presents an accurate indoor localisation approach to provide context aware support for Activities of Daily Living. This paper explores the use of contemporary wearable technology (Google Glass) to facilitate a unique first-person view of the occupants environment. Machine vision techniques are then employed to determine an occupant’s location via environmental object detection within their field of view. Specifically, the video footage is streamed to a server where object recognition is performed using the Oriented Features from Accelerated Segment Test and Rotated Binary Robust Independent Elementary Features algorithm with a K-Nearest Neighbour matcher to match the saved keypoints of the objects to the scene. To validate the approach, an experimental set-up consisting of three ADL routines, each containing at least ten activities, ranging from drinking water to making a meal were considered. Ground truth was obtained from manually annotated video data and the approach was subsequently benchmarked against a common method of indoor localisation that employs dense sensor placement. The paper presents the results from these experiments, which highlight the feasibility of using off-the-shelf machine vision algorithms to determine indoor location based on data input from wearable video-based sensor technology. The results show a recall, precision, and F-measure of 0.82, 0.96, and 0.88 respectively. This method provides additional secondary benefits such as first person tracking within the environment and lack of required sensor interaction to determine occupant location.

The original version of this chapter was inadvertently published with an incorrect chapter pagination 1231–1236 and DOI 10.1007/978-3-319-32703-7_237. The page range and the DOI has been re-assigned. The correct page range is 1237–1242 and the DOI is 10.1007/978-3-319-32703-7_238. The erratum to this chapter is available at DOI: 10.1007/978-3-319-32703-7_260

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-32703-7_260