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Paper Count: 32128
Real-time Tracking in Image Sequences based-on Parameters Updating with Temporal and Spatial Neighborhoods Mixture Gaussian Model
Abstract:Gaussian mixture background model is widely used in moving target detection of the image sequences. However, traditional Gaussian mixture background model usually considers the time continuity of the pixels, and establishes background through statistical distribution of pixels without taking into account the pixels- spatial similarity, which will cause noise, imperfection and other problems. This paper proposes a new Gaussian mixture modeling approach, which combines the color and gradient of the spatial information, and integrates the spatial information of the pixel sequences to establish Gaussian mixture background. The experimental results show that the movement background can be extracted accurately and efficiently, and the algorithm is more robust, and can work in real time in tracking applications.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073521Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1308
 Wren, R. Christopher, A. Azarbayejani, T. Darrell and A, Pentland, "Pfinder: real-time tracking of the human body", IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7), pp.780-785.
 A. Lipton, H. Fujiyoshi and R. Patil, "Moving target classification and tracking from real-time video," in Proc. of IEEE Workshop Application of Computer Vision, Los Alamitos, CA, Oct. 1998.
 A. Shafie, F. Hafiz, and M. Ali, "Motion detection techniques using optical flow," World Academy of Science. Engineering and Technology, 2009, vol.56, pp.559-561.
 C. Stauffer and W. Grimson, "Adaptive background mixture models for real-time tracking," in Proc of IEEE conference on Computer Vision and Pattern Recognition, 1999, pp.246-252.
 A. Elgammal, D. Harwood and L. Davis, "Non-parametric model for background subtraction," ECCV 2000 in computer vision, 2000, pp.751-767.
 O. Javed, K. Shafique and M. Shah. "A hierarchical approach to robust background subtraction using color and gradient information". in Proc. Of IEEE Workshop Motion Video Computing, Dec. 2002, pp.22-27.
 S. Zhang et al, "Dynamic background modeling and subtraction using spatio-temporal local binary patterns," in Proc. of IEEE Int. Conf. on Image Processing, 2008, pp.1556-1559.
 C. Ridder, O. Munkelt and H. Kirchner, "Adaptive background estimation and foreground detection using Kalman-filtering," In Proc. of International Conference on Recent Advances in Mechatronics, ICRAM-95, 1995, pp.193-199,
 Stauffer C, Grimson W. E. L. Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747-757.
 K. Toyama, J. Krumm, B. Brumitt and B. Meyers. "Wallflower: principles and practice of background maintenance," in Proc. of the Seventh IEEE International Conference on Computer, IEEE CS, 1999, pp.255-261.
 Wren, Christopher R, Ali Azarbayejani, Trevor Darrell and Alex Pentland. Pfinder: Real-Time Tracking of the Human Body. IEEE Transactions on Pattern Analysis and Machine Intelligence, July 1997, 19(7): 780-785.
 D. Kim and K. Hong, "Practical background estimation for mosaic blending with patch-based Markov random fields," Pattern Recognition, 2008, 41(7), pp.2145-2155.
 T. Chalidabhongse, K. Kim, D. Harwood and L. Davis. A perturbation method for evaluating background subtraction algorithms. In Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005, pp.15-16.
 R. Cucchiara, C. Grana, M. Piccardi and A. Prati, "Detecting moving objects, ghosts, and shadows in video streams," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, pp.1337-1342.
 X. Gao, T. Boult, F. Coetzee and V. Ramesh, "Error analysis of background subtraction," In Proc. of International Conference on Computer Vision and Pattern Recognition, 2000, pp.503-510.
 M. Heikkila and M. Pietikainen. "A texture-based method for modeling the background and detecting moving objects," IEEE Trans. On Pattern Analysis and Machine Intelligence, 2006, 28(4), pp.657-662.
 J. Pilet, C. Strecha and P. Fua, "Making Background Subtraction Robust to Sudden Illumination Changes," In Proc. Of European Conf. on Computer Vision, 2008, pp.567-580.
 M. Law, M. Figueiredo and A. Jain, "Simultaneous feature selection and clustering using a mixture model," IEEE Trans. Pattern Analysis and Machine Intelligence, 2004, 26(19), pp.1154-1166.