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
 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.
 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.
 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
 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
 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
 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.
 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.
 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
 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