The broad deployment of wearable camera technology in the foreseeable future offers new opportunities for augmented reality applications ranging from consumer (e.g. games) to professional (e.g. assistance). In order to span this wide scope of use cases, a markerless object detection and disambiguation technology is needed that is robust and can be easily adapted to new scenarios. Further, standardized benchmarking data and performance metrics are needed to establish the relative success rates of different detection and disambiguation methods designed for augmented reality applications. Here, we propose a novel object recognition system that fuses state-of-the-art 2D detection with 3D context. We focus on assisting a maintenance worker by providing an augmented reality overlay that identifies and disambiguates potentially repetitive machine parts. In addition, we provide an annotated dataset that can be used to quantify the success rate of a variety of 2D and 3D systems for object detection and disambiguation. Finally, we evaluate several performance metrics for object disambiguation relative to the baseline success rate of a human.
|State||Published - 1 Jan 2014|
|Event||25th British Machine Vision Conference, BMVC 2014 - Nottingham, United Kingdom|
Duration: 1 Sep 2014 → 5 Sep 2014
|Conference||25th British Machine Vision Conference, BMVC 2014|
|Period||1/09/14 → 5/09/14|