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 setup 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.