References
[1]. Chen, L., Li, W., & Yin, W. (2011, July). Joint feature points correspondences and color similarity for robust object tracking. In Multimedia Technology (ICMT), 2011 International Conference on (pp. 403-407). IEEE.
[2]. Dadi, H. S., Pillutla, G. K. M., & Makkena, M. L. (2017). Face Recognition and Human Tracking using GMM, HOG, and SVM in Surveillance Videos. Annals of Data Science, 1-23. Retrieved from https://doi.org/10.1007/s40745-017- 0123-2
[3]. Haritaoglu, I., Harwood, D., & Davis, L. S. (2000). W/sup 4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 809-830.
[4]. Lee, D. S. (2005). Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 827- 832.
[5]. Leibe, B., Schindler, K., Cornelis, N., & Van Gool, L. (2008). Coupled object detection and tracking from static cameras and moving vehicles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(10), 1683- 1698.
[6]. Lipton, A. J., Fujiyoshi, H., & Patil, R. S. (1998, October). Moving target classification and tracking from real-time video. In Applications of Computer Vision, 1998. WACV'98. Proceedings., Fourth IEEE Workshop on (pp. 8- 14). IEEE.
[7]. Masaeli, M., Dy, J. G., & Fung, G. M. (2010). From transformation-based dimensionality reduction to feature selection. In Proceedings of the 27th International Conference on Machine Learning (ICML-10) (pp. 751- 758).
[8]. Milan, A., Roth, S., & Schindler, K. (2014). Continuous energy minimization for multitarget tracking. IEEE Transaction son Pattern Analysis and Machine Intelligence, 36(1), 58-72.
[9]. Paalanen, P., Kamarainen, J. K., Ilonen, J., & Kälviäinen, H. (2006). Feature representation and discrimination based on Gaussian mixture model probability densities-practices and algorithms. Pattern Recognition, 39(7), 1346-1358.
[10]. Rafael C. Gonzalez, Richard E. Woods, & Steven L. Eddins. (2009). Digital Image Processing using MATLAB®. Gatesmark Publishing.
[11]. Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507-2517.
[12]. Sandler, T., Blitzer, J., Talukdar, P. P., & Ungar, L. H. (2009). Regularized learning with networks of features. In Advances in Neural Information Processing Systems (pp. 1401-1408).
[13]. Santosh, D. H., & Mohan, P. K. (2014, April). Tracking manifold objects in motion using Gaussian mixture model and blob analysis. In IEEE International Conference on Convergence of Technology-2014 (pp. 1-7).
[14]. Santosh, D. H., & Mohan, P. K. (2014, May). Multiple objects tracking using extended Kalman filter, GMM, and mean shift algorithm- a comparative study. In Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on (pp. 1484- 1488). IEEE.
[15]. Santosh, D. H., & Mohan, P. K. (2015). Multiple human tracking and prediction under severe occlusions using GMM and Kalman filter. IJAER, 10(11), 29385-29404.
[16]. Stauffer, C. (2003, June). Estimating tracking sources and sinks. In Computer Vision and Pattern Recognition Workshop, 2003. CVPRW'03. Conference on (Vol. 4, pp. 35-42). IEEE.
[17]. Wang, J., Zhao, P., Hoi, S. C., & Jin, R. (2014). Online feature selection and its applications. IEEE Transactions on Knowledge and Data Engineering, 26(3), 698-710.
[18]. Wang, Y., Zhang, J., Wu, L., & Zhou, Z. (2010). Mean shift tracking algorithm based on multi-feature space and grey model. Journal of Computational Information Systems, 6(11), 3731-3739.
[19]. Wu, X., Yu, K., Wang, H., & Ding, W. (2010). Online streaming feature selection. In Proceedings of the 27th International Conference on Machine Learning (ICML- 10) (pp. 1159-1166).
[20]. Xu, Z., King, I., Lyu, M. R. T., & Jin, R. (2010). Discriminative semi-supervised feature selection via manifold regularization. IEEE Transactions on Neural Networks, 21(7), 1033-1047.
