Real-time pose invariant logo and pattern detection

Oliver Sidla; Michal Kottmann; Wanda Benesova

doi:10.1117/12.872977

24 January 2011 Real-time pose invariant logo and pattern detection

Oliver Sidla, Michal Kottmann, Wanda Benesova

Proceedings Volume 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques; 78780C (2011) https://doi.org/10.1117/12.872977
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

The detection of pose invariant planar patterns has many practical applications in computer vision and surveillance systems. The recognition of company logos is used in market studies to examine the visibility and frequency of logos in advertisement. Danger signs on vehicles could be detected to trigger warning systems in tunnels, or brand detection on transport vehicles can be used to count company-specific traffic. We present the results of a study on planar pattern detection which is based on keypoint detection and matching of distortion invariant 2d feature descriptors. Specifically we look at the keypoint detectors of type: i) Lowe's DoG approximation from the SURF algorithm, ii) the Harris Corner Detector, iii) the FAST Corner Detector and iv) Lepetit's keypoint detector. Our study then compares the feature descriptors SURF and compact signatures based on Random Ferns: we use 3 sets of sample images to detect and match 3 logos of different structure to find out which combinations of keypoint detector/feature descriptors work well. A real-world test tries to detect vehicles with a distinctive logo in an outdoor environment under realistic lighting and weather conditions: a camera was mounted on a suitable location for observing the entrance to a parking area so that incoming vehicles could be monitored. In this 2 hour long recording we can successfully detect a specific company logo without false positives.

Citation Download Citation

Oliver Sidla, Michal Kottmann, and Wanda Benesova "Real-time pose invariant logo and pattern detection", Proc. SPIE 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, 78780C (24 January 2011); https://doi.org/10.1117/12.872977

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