Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusing Points and Lines for High Performance Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
ASIFT: A New Framework for Fully Affine Invariant Image Comparison
SIAM Journal on Imaging Sciences
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Proceedings of the 1st international workshop on pervasive eye tracking & mobile eye-based interaction
Fully affine invariant SURF for image matching
Neurocomputing
Gaze guided object recognition using a head-mounted eye tracker
Proceedings of the Symposium on Eye Tracking Research and Applications
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In this paper we investigate the integration of object detection algorithms with eye-tracking data. The emerging technology of lightweight mobile eye-trackers enables realistic in-the-wild user experience experiments. Unfortunately, mobile eye-trackers generate a large amount of video data, which up to now requires manual analysis. This time-consuming and repetitive task renders processing large datasets economically infeasible. Our main contribution is the use of object detection algorithms to perform this analysis task automatically. We compare several object detection algorithms with regard to both speed and accuracy. To prove their functionality, we have recorded an eye-tracker shopping experiment and processed the data using object detection techniques.