TY - GEN
T1 - Colour descriptors for tracking in spatial augmented reality
AU - Kooi, Thijs
AU - De Sorbier, Francois
AU - Saito, Hideo
PY - 2013/4/15
Y1 - 2013/4/15
N2 - Augmented Reality is an emerging research field, that aims for the composition of real and virtual imagery, by means of a camera and display device. Spatial augmented reality employs data projectors to augment the real world. In this setting, traditional tracking methods fall short due to the interference caused by the projector. Recent works assume a calibration process to model the projector and assume continuity in movement of the object being tracked. In this paper we present a tracking-by-detection system that does not require such a procedure and makes use of natural features represented by SIFT descriptors. We evaluate a set of photometric invariants that have previously been shown to improve the performance of object recognition, added to the descriptor to reduce the influence of the projector. We evaluate the descriptors based on precision-recall under projector distortion and the total system based on its tracking performance. Results show tracking is significantly more precise using one of the invariants.
AB - Augmented Reality is an emerging research field, that aims for the composition of real and virtual imagery, by means of a camera and display device. Spatial augmented reality employs data projectors to augment the real world. In this setting, traditional tracking methods fall short due to the interference caused by the projector. Recent works assume a calibration process to model the projector and assume continuity in movement of the object being tracked. In this paper we present a tracking-by-detection system that does not require such a procedure and makes use of natural features represented by SIFT descriptors. We evaluate a set of photometric invariants that have previously been shown to improve the performance of object recognition, added to the descriptor to reduce the influence of the projector. We evaluate the descriptors based on precision-recall under projector distortion and the total system based on its tracking performance. Results show tracking is significantly more precise using one of the invariants.
UR - http://www.scopus.com/inward/record.url?scp=84876007399&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876007399&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37484-5_32
DO - 10.1007/978-3-642-37484-5_32
M3 - Conference contribution
AN - SCOPUS:84876007399
SN - 9783642374838
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 387
EP - 399
BT - Computer Vision - ACCV 2012 International Workshops, Revised Selected Papers
T2 - 11th Asian Conference on Computer Vision, ACCV 2012
Y2 - 5 November 2012 through 9 November 2012
ER -