Detection of positions and recognition of brand logos visible on images captured using mobile devices

Up till now there does not exist an easy, mobile mechanism that allows to easily capture, recognize and count defined, multiple objects that are visible in surroundings of the user. For this purpose, feature detectors (such as SIFT, SURF or BRISK) are utilized to create a database of products box images and extracted keypoints are stored. Existing algorithms based on keypoints analysis do not allow to identify multiple identical logos, due to the fact that a homography calculated on found keypoints can span two or more objects and the result then can be skewed. In this paper a solution to this problem will be shown, that by using a sliding window that joins multiple found keypoints into individual objects, it is possible to correctly detect multiple identical objects. In this paper preliminary results of a mobile framework that allows recognition and counting of visible products in surroundings of the user will be presented.

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[3]  Julien Rabin,et al.  An Analysis of the SURF Method , 2015, Image Process. Line.

[4]  Hua Yang,et al.  Vehicle logo recognition based on Bag-of-Words , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[5]  Jie Li,et al.  Face Recognition Based on Improved SURF , 2013, 2013 Third International Conference on Intelligent System Design and Engineering Applications.

[6]  Jun-Hai Yong,et al.  A new algorithm for Boolean operations on general polygons , 2005, Comput. Graph..

[7]  David G. Lowe,et al.  Shape indexing using approximate nearest-neighbour search in high-dimensional spaces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Roland Siegwart,et al.  BRISK: Binary Robust invariant scalable keypoints , 2011, 2011 International Conference on Computer Vision.

[9]  Feng Wen,et al.  Real-time SIFT-based object recognition system , 2013, 2013 IEEE International Conference on Mechatronics and Automation.

[10]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[11]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[12]  Bo Wang,et al.  Robust traffic sign recognition and tracking for Advanced Driver Assistance Systems , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[13]  Alexandrina Rogozan,et al.  Combining SURF-based local and global features for road obstacle recognition in far infrared images , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.