Human Tracking Using Weighted Running Window Background Model Based GMM

Harihara Santosh Dadi*, Gopala Krishna Mohan Pillutla**, MadhaviLatha Makkena***
Harihara Santosh Dadi *  Gopala Krishna Mohan Pillutla **  Madhavilatha Makkena ***
* PhD Scholar, Department of Electronics and Communication Engineering, JNT University, Hyderabad, India.
** Professor, Institute of Aeronautical College of Engineering, Hyderabad, India.
*** Professor, Department of Electronics and Communication Engineering, JNT University, Hyderabad, India.
DOI : https://doi.org/10.26634/jse.12.2.14066

Abstract

Tracking of humans in video streams is important for many applications. For tracking purposes, many algorithms have come up in the recent years. The most prominent one among all of them is Gaussian Mixture Model (GMM). This algorithm is basically employed for tracking the objects in the Video scene. Later, the algorithm has been modified for the purpose of tracking humans. GMM uses only single rectangular template for tracking an object. In order to track humans specifically, the template has been divided into four regions. The top region is for the head and the remaining regions are for the chest, waist and legs respectively. All the regions are of rectangle shape. Connection has been established among all the regions assuming that all four regions will move at a time for humans. There is only 10% horizontal variation allowed between the regions. The proposed algorithm could handle both partial occlusion and full occlusion. The new algorithm is compared with the tracking system of GMM algorithm. The precision, recall, false alarm per frame, false negatives, false positives and mostly lost are compared with the existing GMM. The time taken for processing a single frame is reduced by using new algorithm when compared with the existing algorithm. Performance metrics show that the Weighted Running Window Background (WRWB) Model Based GMM algorithm out performs when compared with GMM algorithm in terms of time taking.

Keywords

Human Tracking, Gaussian Mixture Model (GMM), Regions, Object Tracking, Dataset.

How to Cite this Article?

Dadi., Pillutla., and Makkena. (2017). Human Tracking Using Weighted Running Window Background Model Based GMM. i-manager’s Journal on Software Engineering, 12(2), 44-54. https://doi.org/10.26634/jse.12.2.14066

References

[1]. Benfold, B., & Reid, I. (2011). Stable multi-target tracking in real-time surveillance video. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 3457-3464). IEEE.
[2]. Birchfield, S. T., & Rangarajan, S. (2005). Spatiograms versus histograms for region-based tracking. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 2, pp. 1158- 1163). IEEE.
[3]. Comaniciu, D., Ramesh, V., & Meer, P. (2003). Kernelbased object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5), 564-577.
[4]. Gonzalez, R. C., Eddins, S. L., & Woods, R. E. (2004). Digital Image Publishing using MATLAB. Prentice Hall.
[5]. Hager, G. D., Dewan, M., & Stewart, C. V. (2004). Multiple kernel tracking with SSD. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on (Vol. 1). IEEE.
[6]. Lee, D. S. (2005). Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 827- 832.
[7]. 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.
[8]. Santosh, D. H., & Mohan, P. K. (2014a). Tracking manifold objects in motion using Gaussian mixture model and blob analysis. In IEEE international conference on convergence of technology-2014 conducted at Pune th th during the period 5 to 7 (pp. 1-7).
[9]. Santosh, D. H., & Mohan, P. K. (2014b). 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.
[10]. 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.
[11]. 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.
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