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An effective client-side object detection method on the android platform

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EN
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EN
This paper presents the analysis of methods for client-side object detection on the Android platform. It describes a phased scheme of the object detection process, identifying its elementary tasks, namely keypoint detection, feature extraction and binary pattern matching. This is followed by a brief overview of the algorithms applied to those tasks in the Android mobile environment. A comparative analysis of those algorithms is performed, with respect to efficiency and object detection quality. The results of this study provide the ability to determine the best possible configuration of object detection algorithms for the Android platform.
Rocznik
Strony
29--38
Opis fizyczny
Bibliogr. 10 poz.
Twórcy
autor
  • Lodz University of Technology, Institute of Information Technology, Wólczańska 215, 90-924 Łódź
  • Lodz University of Technology, Institute of Information Technology, Wólczańska 215, 90-924 Łódź
Bibliografia
  • [1] Wojciechowski, A., Mobile Vision Based Augmented Reality Navigation System, Journal of Applied Computer Science, Vol. 20, No. 1, 2012, pp. 103– 118.
  • [2] Koceski, S., Koceska, N., and Krstev, A., ICT Innovations 2010: Second International Conference, ICT Innovations 2010, Ohrid Macedonia, September 12-15, 2010. Revised Selected Papers, chap. Object Recognition Based on Local Features Using Camera – Equipped Mobile Phone, Springer Berlin Heidelberg, Berlin, Heidelberg, 2011, pp. 296–305.
  • [3] Nascimento, J. and Marques, J., Performance evaluation of object detection algorithms for video surveillance, Multimedia, IEEE Transactions on, Vol. 8, No. 4, Aug 2006, pp. 761–774.
  • [4] Rosten, E. and Drummond, T., Computer Vision – ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I, chap. Machine Learning for High-Speed Corner Detection, Springer Berlin Heidelberg, Berlin, Heidelberg, 2006, pp. 430–443.
  • [5] Leutenegger, S., Chli, M., and Siegwart, R., BRISK: Binary Robust invariant scalable keypoints, In: Computer Vision (ICCV), 2011 IEEE International Conference on, Nov 2011, pp. 2548–2555.
  • [6] Rublee, E., Rabaud, V., Konolige, K., and Bradski, G., ORB: An Efficient Alternative to SIFT or SURF, In: Proceedings of the 2011 International Conference on Computer Vision, ICCV ’11, IEEE Computer Society, Washington, DC, USA, 2011, pp. 2564–2571.
  • [7] Calonder, M., Lepetit, V., Strecha, C., and Fua, P., Computer Vision – ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV, chap. BRIEF: Bi-nary Robust Independent Elementary Features, Springer Berlin Heidelberg, Berlin, Heidelberg, 2010, pp. 778–792.
  • [8] Alahi, A., Ortiz, R., and Vandergheynst, P., FREAK: Fast Retina Keypoint, In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, June 2012, pp. 510–517.
  • [9] Michel Marie Deza, E. D., Encyclopedia of Distance, Springer Berlin Heidelberg, 2013.
  • [10] Schaeffer, C., A Comparison of Keypoint Descriptors in the Context of Pedestrian Detection: FREAK vs. SURF vs. BRISK, 2014.
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-9f7d6de6-dd92-47a4-9d43-2250148e8328
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