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Over the last years, the use of multiple cameras is becoming more and more popular in today’s computer vision systems. Such approach is widely used in many applications, such as navigation of autonomous mobile robots, video surveillance, the movie industry, augmented reality or people tracking and identification systems. Surprisingly, little attention is paid in the literature to the practical calibration procedures that can be employed to map space between various vision systems. Therefore, in this paper a novel approach that allows to map space between cameras with different coordinate systems: Cartesian and polar is presented. The practical problems that occurs in such scenarios are analysed and thoroughly discussed. The authors present stepby-step description of the proposed calibration procedure. A series of experiments were conducted to confirm the correctness of the presented approach and to demonstrate how to apply the developed solution in practical applications. The proposed method does not require any additional equipment beyond the standard calibration chessboard. The achieved results indicate, that for evaluated cameras configuration, the maximum mapping error for the horizontal and vertical axes does not exceed 0.6°. Obtained results are encouraging and useful for development of similar solutions.
Rocznik
Tom
Strony
10--15
Opis fizyczny
Bibliogr. 16 poz., il., fot. kolor., wykr.
Twórcy
autor
- Department of Microelectronics and Computer Science, Lodz University of Technology, ul. Wolczanska 221/223, 90-924 Lodz, Poland
autor
- Department of Microelectronics and Computer Science, Lodz University of Technology, ul. Wolczanska 221/223, 90-924 Lodz, Poland
autor
- Department of Microelectronics and Computer Science, Lodz University of Technology, ul. Wolczanska 221/223, 90-924 Lodz, Poland
autor
- Department of Microelectronics and Computer Science, Lodz University of Technology, ul. Wolczanska 221/223, 90-924 Lodz, Poland
Bibliografia
- [1] M. Aranda, G. López-Nicolás, C. Sagüés, and Y. Mezouar, “Formation control of mobile robots using multiple aerial cameras,” IEEE Transactions on Robotics, vol. 31, no. 4, pp. 1064–1071, Aug 2015.
- [2] C. M. Huang and L. C. Fu, “Multitarget visual tracking based effective surveillance with cooperation of multiple active cameras,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 41, no. 1, pp. 234–247, Feb 2011.
- [3] K. S. Kumar, S. Prasad, P. K. Saroj, and R. C. Tripathi, “Multiple cameras using real time object tracking for surveillance and security system,” pp. 213–218, Nov 2010.
- [4] H. Kim, R. Sakamoto, K. Kogure, and I. Kitahara, “Cinematized reality: Cinematographic 3d video system for daily life using multiple outer/inner cameras,” pp. 168–168, June 2006.
- [5] H. Saito, N. Inamoto, and S. Iwase, “Sports scene analysis and visualization from multiple-view video,” vol. 2, pp. 1395–1398 Vol.2, June 2004.
- [6] I. Everts, N. Sebe, and G. A. Jones, “Cooperative object tracking with multiple ptz cameras,” pp. 323–330, Sept 2007.
- [7] H. U. Chae, S.-J. Kang, and K. H. Jo, “Identification of a human using accorded blobs on the varied region from image sequence by multiple cameras,” pp. 1887–1891, Oct 2008.
- [8] P. Perek, D. Makowski, A. Mielczarek, A. Napieralski, and P. Sztoch, “Towards automatic calibration of stereoscopic video systems,” pp. 134– 137, June 2015.
- [9] J. Li, P. Duan, and J. Wang, “Binocular stereo vision calibration experiment based on essential matrix,” Computer and Communications (ICCC), 2015 IEEE International Conference on, pp. 250–254, Oct 2015.
- [10] I. H. Chen and S. J. Wang, “An efficient approach for the calibration of multiple ptz cameras,” IEEE Transactions on Automation Science and Engineering, vol. 4, no. 2, pp. 286–293, April 2007.
- [11] K. Okumura, H. Oku, and M. Ishikawa, “High-speed gaze controller for millisecond-order pan/tilt camera,” pp. 6186–6191, May 2011.
- [12] G. Bradski, Dr. Dobb’s Journal of Software Tools, 2000.
- [13] T. M., “How to check if two line segments intersect - bounding boxes,” 2013.
- [14] “Leica disto d3a bt user manual,” 2015.
- [15] P. Nowak, W. Sankowski, and P. Krotewicz, “3d face and hand scans acquisition system dedicated for multimodal biometric identification,” June 2016.
- [16] R. B. Rusu and S. Cousins, “3d is here: Point cloud library (pcl),” May 9-13 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-87adf94d-35c5-4a09-afa6-c6e49a3251c6