Hybrid Tracking using Gravity Aligned Edges (2013)
Type of ContentConference Contributions - Published
PublisherUniversity of Canterbury. Computer Science and Software Engineering
University of Canterbury. Human Interface Technology Laboratory
AuthorsWilliams, S., Green, R., Billinghurst, M.show all
We have developed a hybrid tracking algorithm for mobile outdoor augmented reality (AR) applications. Our approach combines inertial sensors and camera video to improve global bearing calculations. Prior research in this area has focused on gravity aware feature descriptors, but we expand this to efficient full-frame vertical edge detection. We discuss our implementation and evaluate it’s performance on an iPhone 5, which reveals that our approach is over 100 times faster than existing feature alignment algorithms and can improve tracking with only 2-4ms of additional processing per frame on current generation mobile phones.
CitationWilliams, S., Green, R., Billinghurst, M. (2013) Hybrid Tracking using Gravity Aligned Edges. Christchurch, New Zealand: CHINZ 2013: 14th Annual Conference of the New Zealand Chapter of the ACM Special Interest Group on Computer-Human Interaction, 15-16 Nov 2013.
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Keywordsaugmented reality; tracking
ANZSRC Fields of Research08 - Information and Computing Sciences::0801 - Artificial Intelligence and Image Processing::080111 - Virtual Reality and Related Simulation
08 - Information and Computing Sciences::0805 - Distributed Computing::080502 - Mobile Technologies