Comparison of keypoint detection methods for indoor and outdoor scene recognition

• Autorzy: Libal U. , Łoziuk Ł.

Identyfikatory

DOI

10.14313/JAMRIS_4-2017/32

• Języki publikacji: EN

Abstrakty

EN

We describe an experimental study, based on several million video scenes, of seven keypoint detection algorithms: BRISK, FAST, GFTT, HARRIS, MSER, ORB and STAR. It was observed that the probability distributions of selected keypoints are drastically different between indoor and outdoor environments for all algorithms analyzed. This paper presents a simple method for distinguishing between indoor and outdoor environments in a video sequence. The proposed method is based on the central location of keypoints in video frames. This has lead to a universally effective indoor/outdoor environment recognition method, and may prove to be a crucial step in the design of robotic control algorithms based on computer vision, especially for autonomous mobile robots.

Słowa kluczowe

EN

machine vision; keypoint; UAVs; scene recognition

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2017
• Tom: Vol. 11, No. 4
• Strony: 7--14
• Opis fizyczny: Bibliogr. 21 poz., rys.

Twórcy

autor

Libal U.

autor

Łoziuk Ł.

Bibliografia

• [1] S. Srinivasan, L. Kanal, ”Qualitative landmark recognition using visual cues”, Pattern Recognition Letters, vol. 18, no.11-13, 1997, 1405–1414. DOI: 10.1016/S0167-8655(97)00142-6.
• [2] M. Bosse, R. Zlot, ”Keypoint design and evaluation for place recognition in 2D lidar maps”, Robotics and Autonomous Systems, vol. 57, no.12, 2009, 1211–1224. DOI: 10.1016/j.robot.2009.07.009.
• [3] P. Espinace, T. Kollar, N. Roy, A. Soto, ”Indoor scene recognition by a mobile robot through adaptive object detection”, Robotics and Autonomous Systems, vol. 61, no. 9, 2013, 932–947. DOI: 10.1016/j.robot.2013.05.002.
• [4] A. Vailaya, A.K. Jain, H. Zhang, ”On image classifiication: city images vs. landscapes”, Pattern Recognition, vol. 31, no. 12, 1998, 1921-1935. DOI: 10.1016/S0031-3203(98)00079-X.
• [5] M. Calonder, V. Lepetit, C. Strecha, P. Fua, ”BRIEF: Binary Robust Independent Elementary Features”. In: European Conference on Computer Vision(ECCV), Heraklion, Crete, Greece, September 5-11, Proceedings - Part IV, 778–792, 2010. DOI: 10.1007/978-3-642-15561-1_56.
• [6] Itseez. Open CV Library. http://docs.opencv. org/modules/features2d/doc/common_interfaces_of_feature_detectors.html,Accessed: 2017-04-30.
• [7] G.L. Foresti, C. Micheloni, ”Real-time videosurveillance by an active camera”, Ottavo Convegno Associazione Italiana Intelligenza Artifii ciale (AI*IA)– Workshop sulla Percezione e Visionenelle Macchine, Universita di Siena, 2002. DOI: 10.1.1.19.4723.
• [8] A. Quattoni, A. Torralba, ”Recognizing indoor scenes”. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Los Alamitos, CA, USA, 413–420, 2009. DOI: 10.1109/CVPRW.2009.5206537.
• [9] S. Leutenegger, M. Chli, R.Y. Siegwart, ”BRISK: Binary Robust invariant scalable keypoints”. In: IEEE International Conference on Computer Vision (ICCV), 2548–2555, 2011. DOI: 10.1109/ICCV.2011.6126542.
• [10] J. Shi, C. Tomasi, ”Good features to track”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR),1994, 593–600. DOI: 10.1109/CVPR.1994.323794.
• [11] E. Rosten, T. Drummond, ”Fusing Points and Lines for High Performance Tracking”, IEEE International Conference on Computer Vision (ICCV), 17-20 October 2005, Beijing, China, 1508–1515, 2005. DOI: 10.1.1.451.4631.
• [12] C. Harris, M. Stephens, ”A Combined Corner and Edge Detector”. In: Alvey Vision Conference, (AVC), Manchester, UK, September, 1-6, 1988. DOI: 10.5244/C.2.23
• [13] J. Matas, O. Chum, M. Urban, T. Pajdla, ”Robust wide-baseline stereo from maximally stable extremal regions”, Image Vision Computing, vol. 22, no. 10, 2004, 761-767. DOI: 10.1016/j.imavis.2004.02.006.
• [14] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, L. Van Gool, ”A Comparison of Affine Region Detectors”, International Journal of Computer Vision, vol. 65, no. 1– 2, 2005, 43–72. DOI: 10.1007/s11263-005-3848-X.
• [15] E. Rublee, V. Rabaud, K. Konolige, G. Bradski, ”ORB: An effiicient alternative to SIFT or SURF”, IEEE International Conference on Computer Vision, IEEE Computer Society, Los Alamitos, CA, USA, 2564– 2571, 2011. DOI:10.1109/ICCV.2011.6126544.
• [16] M. Agrawal, K. Konolige, M.R. Blas, ”CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching”, European Conference on Computer Vision (ECCV), Marseille, France, October 12–18, 2008, Proceedings – Part IV, 102–115, 2008. DOI: 10.1007/978-3-540-88693-8_8.
• [17] Ł. Łoziuk. Video Analysis Algorithms – Software. https://github.com/bitcoinsoftware/video AlgorithmsAnalysis, Accessed: 2017- 08-30.
• [18] A. Lawniczak, B. Di Stefano, J. Ernst, ”Stochastic Model of Cognitive Agents Learning to Cross a Highway”, Stochastic Models, Statistics and Their Applications, Springer Proceedings in Mathematics and Statistics, 122:319–326, 2015. DOI: 10.1007/978-3-319-13881-7_35.
• [19] D. Lopez De Luise, G. Barrera, S. Franklin, ”Robot Localization Using Consciousness”, Journal of Pattern Recognition Research, 6(1), 2011, 96–119. DOI: 10.13176/11.257.
• [20] R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classifiication, 2nd Edition, Wiley-Interscience, 2000.
• [21] C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. DOI: 10.1117/1.2819119.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).