LEDs based video camera pose estimation

• Autorzy: Sudars K. , Cacurs R. , Homjakovs I. , Judvaitis J.

Identyfikatory

DOI

10.1515/bpasts-2015-0102

• Języki publikacji: EN

Abstrakty

EN

For 3D object localization and tracking with multiple cameras the camera poses have to be known within a high precision. The paper evaluates camera pose estimation via a fundamental matrix and via the known object in environment of multiple static cameras. A special feature point extraction technique based on LED (Light Emitting Diodes) point detection and matching has been developed for this purpose. LED point detection has been solved searching local maximums in images and LED point matching has been solved involving patterned time functions for each light source. Emitting LEDs have been used as sources of known reference points instead of typically used feature point extractors like ORB, SIFT, SURF etc. In such a way the robustness of pose estimation has been obtained. Camera pose estimation is significant for object localization using the networks with multiple cameras which are going to an play increasingly important role in modern Smart Cities environments.

Słowa kluczowe

EN

camera pose estimation; image keypoint detection and matching; 3D point reconstruction; object localization and tracking

PL

oszacowanie ustawienia kamery; rekonstrukcja modelu 3D; lokalizacja obiektu; śledzenie obiektu

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2015
• Tom: Vol. 63, nr 4
• Strony: 897--905
• Opis fizyczny: Bibliogr. 27 poz., rys., fot., wykr.

Twórcy

autor

Sudars K.

• Institute of Electronics and Computer Science, 14 Dzerbenes St., LV-1006, Riga, Latvia

autor

Cacurs R.

• Institute of Electronics and Computer Science, 14 Dzerbenes St., LV-1006, Riga, Latvia

autor

Homjakovs I.

• Institute of Electronics and Computer Science, 14 Dzerbenes St., LV-1006, Riga, Latvia

autor

Judvaitis J.

• Institute of Electronics and Computer Science, 14 Dzerbenes St., LV-1006, Riga, Latvia

Bibliografia

• [1] A. Zisserman and R. Hartley, “Multiple view geometry in computer vision”, Cambridge University Press, Cambridge, 2003.
• [2] E. Rijpkema, K. Muthukrishnan, S. Dulman, and K. Langendoen, “Pose estimation with radio-controlled visual markers”, IEEE 7 th Int. Conf. on Mobile AdHoc and Sensor Systems 1, 658-665 (2010).
• [3] F. Haranz, K. Muthukrishnan, and K. Langendoen, “Camera pose estimation using particle filters”, IEEE Int. Conf. on Indoor Positioning and Indoor Navigation 1, 1-8 (2011).
• [4] M. Faessler, E. Mueggler, K. Schwabe, and D. Scaramuzza, “A Monocular pose estimation system based on infrared LEDs”, IEEE Int. Conf. on Robotics and Automation (ICRA) 1, 907-913 (2014).
• [5] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: an efficient alternative to SIFT or SURF”, http://www.vision.cs.chubu.ac.jp/CV-R/pdf/Rubleeiccv2011.pdf.
• [6] E. Rosten, “FAST corner detection”, http://www.edwardrosten.com/work/fast.html.
• [7] M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “BRIEF: binary robust independent elementary features”, Computer Vision ECCV 2010, Lecture Notes in Computer Science 6314, 778-792 (2010).
• [8] R.W. Hamming, “Error detecting and error correcting codes”, The Bell System Technical J. 29 (2), 147-160 (1950).
• [9] J.E. Bresenham, “Algorithm for computer control of a digital plotter”, IBM Systems J. 4 (1), 25-30 (1965).
• [10] P.L. Rosin, “Measuring corner properties”, Computer Vision and Image Understanding 73 (2), 291-307 (1999).
• [11] X. Armangue and J. Salvi, “Overall view regarding fundamental matrix estimation”, Image and Vision Computing 21 (2), 5-220 (2003).
• [12] R.I. Hartley, “Estimation of relative camera positions for uncalibrated cameras”, Proc. Eur. Conf. Computer Vision 1, CDROM (1992).
• [13] O.D. Faugeras, “What can be seen in three dimensions with an uncalibrated stereo rig?”, Lecture Notes in Computer Science 588, 563-578 (1992).
• [14] G. Strang, “Introduction to Linear Algebra”, Wellesley- Cambridge Press, Cambridge, 2009.
• [15] H.C. Longuet-Higgins, “A computer algorithm for reconstructing a scene from two projections”, Nature 293 (5828), 133-135 (1981).
• [16] M. Dhome, M. Richetin, and J.-T. Lapreste, “Determination of the attitude of 3D objects from a single perspective view”, IEEE Trans. Pattern Analysis ad Machine Intelligence 11 (12), 1265-1278 (1989).
• [17] R. Horaud, B. Conio, O. Leboulleux, and B. Lacolle, “An analytic solution for the perspective 4-point problem“ Computer Vision, Graphics, and Image Processing 47 (1), 33-44 (1989).
• [18] R.M. Harlick, D. Lee, K. Ottenburg, and M. Nolle, “Anallysis and solutions of the three point perspective pose estimation problem”, Conf. Computer Vision and Pattern Recognition 1, 592-598 (1991).
• [19] L. Quan and Z. Lan, “Linear N-point camra pose determination”, IEEE Transaction on Pattern Analysis and Machine Intelligence 21 (7), 774-780, (1991).
• [20] B. Triggs, “Camera pose and calibration from 4 or 5 known 3D points”, Int. Conf. Computer Vision 1, 278-284 (1999).
• [21] P.D. Fiore, “Efficient linear solution of exterior orientation”, IEEE Trans. on Pattern Analysis and Machine Intelligence 23 (1), 140-148 (2001).
• [22] V. Lepetit, F. Moreno-Noguer, and P. Fua, “EPnP: an accurate O(n) solution to the PnP problem”, Int. J. Computer Vision 81 (2), 155-166 (2009).
• [23] D.G. Lowe, “Fitting parameterized three-dimensional models to images”, IEEE Trans. and Pattern Analysis and Machine Inteligence 13(5), 441-450 (1991).
• [24] D. DeMenthon and L.S. Davis, “Model-based object pose in 25 lines of code”, Int. J. Computer Vision 15, 123-141 (1995).
• [25] C.-P. Lu, G.D. Hager, and E. Mjolsness, “Fast and globally convergent pose estimation from video images”, IEEE Trans. 22 (6), 610-622 (2000).
• [26] M.A. Fischler and R.C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography”, Comm. Assoc. Comp. Mach. 14 (6), 381-395 (1981).
• [27] P.D. Sampson, “Fitting conic sections to ’very scattered’ data: an iterative refinement of the Bookstein algorithm”, Computer Vision, Graphics, and Image Processing 18, 97-108 (1982).