Multiple Tracking of Moving Objects with Kalman Filtering and PCA-GMM Method

DOI: 10.4236/iim.2013.52006   PDF   HTML   XML   4,465 Downloads   6,986 Views   Citations


In this article we propose to combine an integrated method, the PCA-GMM method that generates a relatively improved segmentation outcome as compared to conventional GMM with Kalman Filtering (KF). The combined new method the PCA-GMM-KF attempts tracking multiple moving objects; the size and position of the objects along the sequence of their images in dynamic scenes. The obtained experimental results successfully illustrate the tracking of multiple moving objects based on this robust combination

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E. Noureldaim, M. Jedra and N. Zahid, "Multiple Tracking of Moving Objects with Kalman Filtering and PCA-GMM Method," Intelligent Information Management, Vol. 5 No. 2, 2013, pp. 42-47. doi: 10.4236/iim.2013.52006.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] N. Friedman and S. Russell, “Image Segmentation in Video Sequences,” 13th Conference on Uncertainty in Artificial Intelligence, 1997, pp. 175-181.
[2] C. Stauffer and W. Grimson “Adaptive Background Mixture Models for Real-Time Tracking,” Proceedings of IEEE Computer Vision and Pattern Recognition, 1999, pp. 246-252.
[3] C. Stauffer and W. Grimson, “Learning Patterns of Activity Using Real-Time Tracking,” IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 22, No. 8, 2000, pp. 747-757. doi:10.1109/34.868677
[4] W. Grimson, C. Stauffer, R. Romano and L. Lee, “Using Adaptive Tracking to Classify and Monitor Activities in a Site,” Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998.
[5] T. Bouwmans, F. El Baf and B. Vachon, “Background Modeling Using Mixture of Gaussians for Foreground Detection,” Recent Patents on Computer Science, 2008, pp. 219-237.
[6] Y. Zhu and K. Fujimura, “Driver Face Tracking Using Gaussian Mixture model (GMM),” Proceedings of IEEE on Intelligent Vehicles Symposium, 2003, pp. 587-592.doi:10.1109/IVS.2003.1212978
[7] P. KaewTraKulPong and R. Bowden, “An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection,” 2nd European Workshop on Advanced Video Based Surveillance Systems, September 2001.
[8] S. Cheung and C. Kamath, “Robust Background Subtraction with Foreground Validation for Urban Traffic Video,” EURASIP Journal on Applied Signal Processing, Vol. 14, 2005, pp. 2330-2340.doi:10.1155/ASP.2005.2330
[9] R. Chen and J. S. Liu, “Mixture Kalman Filters,” Journal of the Royal Statistical Society, Series B-Statistical Methodology, Vol. 62, No. 3, 2000, pp. 493-508.doi:10.1111/1467-9868.00246
[10] A. Yilmaz, O. Javed and M. Shah, “Object Tracking: A Survey,” ACM Computing Surveys, Vol. 38, No. 4, 2006, pp. 1-45. doi:10.1145/1177352.1177355
[11] K. Quast and A. Kaup, “AUTO GMM-SAMT: An Automatic Object Tracking System for Video Surveillance in Traffic Scenarios,” EURASIP Journal on Image and Video Processing, Vol. 2011, 2011, pp. 2-14.
[12] H. Moon and P. Jonathan, “Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms,” Perception, Vol. 30, No. 3, 2001, pp. 303-321.doi:10.1068/p2896
[13] H. Kim, D. Kim and S. Yang, “An Efficient Model Order Selection for PCA Mixture Model,” Pattern Recognition, Vol. 24, No. 9-10, 2003, pp. 1385-1393.doi:10.1016/S0167-8655(02)00379-3
[14] B. Antoni, M. Vijay and V. Nuno, “Generalized StaufferGrimson Background Subtraction for Dynamic Scenes,” Journal of Machine Vision and Applications, Vol. 22, No. 5, 2011, pp. 751-766.
[15] I. Gómez, D. Olivieri, X. Vila and S. Orozco, “Simple Human Gesture Detection and Recognition Using a Feature Vector and a Real-Time Histogram Based Algorithm,” Journal of Signal and Information Processing, Vol. 2, No. 4, 2011, pp. 279-286.doi:10.4236/jsip.2011.24040
[16] Y. Sheikh and M. Shah, “Bayesian Modeling of Dynamic Scenes for Object Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 1, 2005, pp. 1778-1792. doi:10.1109/TPAMI.2005.213
[17] Y. Yong and W. Ya-Fei, “Moving Object Detection Based on Spatial Local Correlation,” Opto-Electronic Engineering, Vol. 36, No. 2, 2009.
[18] R. Hassanpour, A. Shahbahrami and S. Wong, “Adaptive Gaussian Mixture Model for Skin Color Segmentation,” Proceeding of World Academic of Science Engineering and Technology, Vol. 31, 2008, pp.1307-6884.
[19] G. Rajkumar, K. Srinivasarho and P. Sribivasa, “Image Segmentation Method Based on Finite Doubly Truncated Bivariate Gaussian Mixture Model with Hierarchical Clustering,” International Journal of Computer Science Issues, Vol. 8, No. 2, 2011, pp. 1694-0814.
[20] K. Panta, “Novel Data Association Schemes for the Probability Hypothesis Density Filter,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 2, 2007, pp. 556-570. doi:10.1109/TAES.2007.4285353
[21] N. Emadeldeen, M. Jedra and N. Zahid, “On Segmentation of Moving Objects by Integrating PCA Method with the Adaptive Background Model,” Journal of Signal and Information Processing, Vol. 3, No. 3, 2012, pp. 387-393. doi:10.4236/jsip.2012.33051
[22] S. Grewal and P. Andrews, “Kalman Filtering Theory and Practice Using Matlab Grewal,” 2nd Edition, John Wiley & Sons Inc., New York, 2001.

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