Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
first last
cannonical link button

http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-56080b54-e63e-4319-8939-ee8cada1a6bc

Czasopismo

Acta Mechanica et Automatica

Tytuł artykułu

Vision analysis system for autonomous landing of micro drone

Autorzy Skoczylas, M. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN This article describes a concept of an autonomous landing system of UAV (Unmanned Aerial Vehicle). This type of device is equipped with the functionality of FPV observation (First Person View) and radio broadcasting of video or image data. The problem is performance of a system of autonomous drone landing in an area with dimensions of 1m × 1m, based on CCD camera coupled with an image transmission system connected to a base station. Captured images are scanned and landing marker is detected. For this purpose, image features detectors (such as SIFT, SURF or BRISK) are utilized to create a database of keypoints of the landing marker and in a new image keypoints are found using the same feature detector. In this paper results of a framework that allows detection of definedmarker for the purpose of drone landing field positioning will be presented.
Słowa kluczowe
PL bezzałogowy obiekt latający   dron   analiza obrazów   śledzenie obiektu   SURF   CAMSHIFT  
EN unmanned aerial vehicle   micro drone   image analysis   CCD camera   keypoints descriptors   SIFT   SURF   BRISK   object tracking   CAMSHIFT  
Wydawca Oficyna Wydawnicza Politechniki Białostockiej
Czasopismo Acta Mechanica et Automatica
Rocznik 2014
Tom Vol. 8, no. 4
Strony 199--203
Opis fizyczny Bibliogr. 18 poz., rys., wykr.
Twórcy
autor Skoczylas, M.
Bibliografia
1. Andriluka M., Schnitzspan P., Meyer J., Kohlbrecher S., Petersen K., Stryk O., Roth S., Schiele B. (2010), Vision based victim detection from unmanned aerial vehicles, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
2. Bay H., Ess A., Tuytelaars T., Van Gool L. (2008), Speeded-Up Robust Features (SURF), Comput. Vis. Image Underst., Vol. 110(3), 346–359.
3. Besbes B., Apatean A., Rogozan A., Bensrhair A. (2010), Combining SURF-based local and global features for road obstacle recognition in far infrared images. 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 1869 – 1874.
4. Bradski G. (2000), The OpenCV Library, Dr. Dobb’s Journal of Software Tools.
5. Bradski R. (1998), Computer vision face tracking for use in a perceptual user interface, Proceedings of the Fourth IEEE Workshop on Applications of Computer Vision, 214-219.
6. Dalal N., Triggs B. (2005), Histograms of oriented gradients for human detection, Computer Vision and Pattern Recognition, 2005. CVPR 2005, IEEE Computer Society Conference , Vol. 1, 886–893.
7. Ding D., Yoon J., Lee C. (2012), Traffic sign detection and identification using SURF algorithm and GPGPU, SoC Design Conference (ISOCC), 506–508.
8. Leutenegger S., Chli M., Siegwart R. (2011), BRISK: Binary Robust invariant scalable keypoints, Computer Vision, IEEE International Conference, 2548–2555.
9. Li H., Xu T., Li J., Zhang L. (2013), Face recognition based on improved SURF, Third International Conference on Intelligent System Design and Engineering Applications (ISDEA), 755 – 758.
10. Lowe D. (2004), Distinctive Image Features from Scale-Invariant Keypoints, Int. J. Comput. Vision, Vol. 60(2), 91–110.
11. Lukashevich P., Zalesky B., Ablameyko S. (2011), Medical image registration based on surf detector, Pattern Recognit. Image Anal., Vol. 21(3), 519–521.
12. Oyallon E., Rabin J. (2013), An analysis and implementation of the SURF method, and its comparison to SIFT, Image Processing On Line, 1-31.
13. Pan J., Chen W., Peng W. (2013), A new moving objects detection method based on improved SURF algorithm, 25th Chinese Control and Decision Conference (CCDC), 901–906.
14. Shaker M., Smith M. N. R., Shigang Y., Duckett T. (2010), Visionbased landing of a simulated unmanned aerial vehicle with fast reinforcement learning, International Conference on Emerging Security Technologies (EST), 183–188.
15. Skoczylas M., Rakowski W., Cherubini R., Gerardi S. (2011), Unstained viable cell recognition in phase-contrast microscopy, Opto-Electronics Review, Vol. 19(3), 307–319.
16. Wang Z., Xiao H., He W., Wen F., Yuan K. (2013), Real-time SIFTbased object recognition system, IEEE International Conference on Mechatronics and Automation (ICMA), 1361 – 1366.
17. Yu S., Zheng S., Yang H., Liang L. (2013), Vehicle logo recognition based on bag-of-words, 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 353 – 358.
18. Zheng Z., Zhang H., Wang B., Gao Z. (2012), Robust traffic sign recognition and tracking for advanced driver assistance systems, International IEEE Conference on Intelligent Transportation Systems (ITSC), 704 – 709.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-56080b54-e63e-4319-8939-ee8cada1a6bc
Identyfikatory