Accès libre

Moving Target Detection Based On Global Motion Estimation In Dynamic Environment

À propos de cet article

Citez

M. Betke, E. Haritaoglu and L. S. Davis, “Real-time multiple vehicle detection and tracking from a moving vehicle”, Machine Vision and Applications, December 2000, pp. 69-83.10.1007/s001380050126 Search in Google Scholar

H. Tao, H. S. Sawhney and R. Kumar, “Object tracking with Bayesian estimation of dynamic layer representation”, IEEE Trans. PAMI,Vol. 24,No. 1,2002, pp. 75-89.10.1109/34.982885 Search in Google Scholar

R. Pless, T. Brodsky and Y. Aloimonos, “Detecting independent motion: The statistics of temporal continuity”, IEEE Trans. PAMI, Vol. 22, No. 8, 2000, pp. 768-773.10.1109/34.868679 Search in Google Scholar

W. Bell, P. Felzenszwalb and D. Huttenlocher, “Detection and Long Term Tracking of Moving Object in Aerial Video”, http://www.cs.cornell.edu/vision/wbell/identtracker, March 1999. Search in Google Scholar

J. Konrad, “Motion detection and estimation”, Handbook of Image and Video Processing (A. Bovik, ed.), ch. 3.10, Academic Press, 2000, pp. 207-225. Search in Google Scholar

Yosi Keller and Amir Averbuch, “Fast motion estimation using bidirectional gradient methods”, IEEE Transactions on Image Processing, Vol. 13, No. 8, August, 2004,pp.1042-1054.10.1109/TIP.2004.823823 Search in Google Scholar

G. Sorwar, M. Murshed and L. Dooley, “A Fully Adaptive Distance-dependent Thresholding Search (FADTS) Algorithm for Performance-management Motion Estimation”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 17, No. 4,2007, pp.429-440.10.1109/TCSVT.2006.888816 Search in Google Scholar

R. T. Collins, A. J. Lipton and T. Kanada, et.al, “A System for Video Surveillance and Monitoring”,VSAM Final Report, Technical report, CMU-RI-TR-00-12, Robotics Institute, Carnegie Mellon University, 2000. Search in Google Scholar

Ali Saad and Shah Mubarak,”COCOA-Tracking in Aerial Imagery”, SPIE, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications, 2006, pp.110-118, Orlando.10.1117/12.667266 Search in Google Scholar

K. Haris, S. N. Efstratiadis, N. Maglaveras and C. Pappas, “Image noise reduction based on local classification and iterated conditional modes”, Proc. IWISP, Manchester, U.K, November 1996.10.1016/B978-044482587-2/50076-8 Search in Google Scholar

R. Guo and S. M. Pandit, “Automatic Threshold Selection based on Histogram Modes and a Discriminant Criterion”, Machine Vision and Application, No. 10, 1998, pp. 331-338.10.1007/s001380050083 Search in Google Scholar

N. Otsu, “A Threshold Selection Method from Gray-level Histograms”, IEEE Transactions on Systems, Man and Cybernetics, Vol. 9,No. 1,1979, pp. 62-66.10.1109/TSMC.1979.4310076 Search in Google Scholar

E. R. Dougherty and C. R. Giardina, “Mathematical Methods for Artificial Intelligence and Autonomous Systems”, Prentice-Hall, 1988, pp.319. Search in Google Scholar

C. Anderson, P. Burt and W. G. van der, “Change detection and tracking using Pyramid transformation techniques”, Proceedings of SPIE-Intelligent Robots and Computer Vision, Vol. 579,1985, pp.72-78.10.1117/12.950785 Search in Google Scholar

A. J. Lipton, H. Fujiyoshi and R. S. Patil, “Moving Target Classification and Tracking from Real-Time Video”, Proc. Fourth IEEE Workshop on Application and Computer Vision, 1998, pp.8-14. Search in Google Scholar

Y. H. Yang and M. D. Levine, “The background Primal sketch: An approach for tracking moving objects”, Machine Vision and Application, No. 5, 1992, pp.17-34.10.1007/BF01213527 Search in Google Scholar

J. B. Kim and H. J. Kim, “Efficient region-based motion segmentation for video monitoring system”, Pattern Recognition Letters, Vol. 24, No.1, 2003, pp.113-128.10.1016/S0167-8655(02)00194-0 Search in Google Scholar

J. Barron, D. Fleet and S. Beauchemin, “Performance of optical flow techniques”, International Journal of Computer Vision, Vol. 12, No.1, 1994, pp. 42-77.10.1007/BF01420984 Search in Google Scholar

S. Brandt, “Maximum likelihood robust regression with known and unknown residual models”, Proc. of the ECCV2002, pp.97-102. Search in Google Scholar

A. Smolic and I. R.Ohm, “Robust global motion estimation using a simplified M-estimator approach”, Proceedings of the IEEE International Conference on Image Processing, 2000, pp.868-871. Search in Google Scholar

P. Torr and D. Murray, “The development and comparison of robust methods for estimating the fundamental matrix”, Int. Journal of Computer Vision, Vol. 24, No.3, 1997, pp. 271-300.10.1023/A:1007927408552 Search in Google Scholar

Z. Y. Zhang, “Determining the epipolar geometry and its uncertainty: A review”, Int. Journal of Computer Vision, Vol. 27, No.2, 1998, pp. 161-195.10.1023/A:1007941100561 Search in Google Scholar

Kim Yeon-Ho and A. C. Kak, “Error Analysis of Robust Optical Flow Estimation by Least Median of Squares Methods for the Varing Illumination Model”, IEEE Trans. PAMI, Vol. 28, No.9, 2006, pp. 1408-1435.10.1109/TPAMI.2006.18516929729 Search in Google Scholar

R. Subbarao and P. Mee, “Beyond RANSAC: User Independent Robust Regression”, Computer Vision and Pattern Recognition Workshop, 2006, pp.101-105. Search in Google Scholar

eISSN:
1178-5608
Langue:
Anglais
Périodicité:
Volume Open
Sujets de la revue:
Engineering, Introductions and Overviews, other