Human Tracking Under Severe Occlusions

Harihara Santosh Dadi*, Gopala Krishna Mohan Pillutla**, Madhavi latha Makkena***
* Associate Professor, Aditya Institute of Technology and Management, Hyderabad, India.
** Professor, Institute of Aeronautical College of Engineering, Hyderabad, India.
*** Professor, Department of Electronics and Communication Engineering, JNT University, Telangana, India.
Periodicity:July - September'2017


Human tracking using surveillance cameras is a demanding topic of research now-a-days. Tracking and recognizing the human is much more challenging. There are many existing methods for tracking humans based on shapes and motion. In this paper, a novel algorithm for tracking human under occlusions is introduced. Gaussian Mixture Model (GMM) is used for tracking the human, which performs well under different occlusions. The new algorithm produces excellent results in case the human is occluded by another human and is obstructed by some other human, and also when there are partial occlusions. Experimental results show that the new algorithm outperforms in tracking the humans in these three cases.


GMM, Occlusion, Human Tracking

How to Cite this Article?

Dadi, H, S., Pillutla, G, K, H., and Makkena, M, L. (2017). Human Tracking Under Severe Occlusions. i-manager’s Journal on Software Engineering, 12(1), 29-37.


[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 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.

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