Scale Adaptive Object Tracker with Occlusion Handling

Full Text (PDF, 944KB), PP.27-35

Views: 0 Downloads: 0

Author(s)

Ramaravind K M 1,* Shravan T R 2 S.N. Omkar 2

1. National Institute of Technology, Tiruchirappalli, India

2. Indian Institute of Science, Bangalore, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2016.01.03

Received: 15 Sep. 2015 / Revised: 22 Oct. 2015 / Accepted: 26 Nov. 2015 / Published: 8 Jan. 2016

Index Terms

Object Tracking, Mean-shift, RBF Neural Networks, Scale estimation, Occlusion handling

Abstract

Real-time object tracking is one of the most crucial tasks in the field of computer vision. Many different approaches have been proposed and implemented to track an object in a video sequence. One possible way is to use mean shift algorithm which is considered to be the simplest and satisfactorily efficient method to track objects despite few drawbacks. This paper proposes a different approach to solving two typical issues existing in tracking algorithms like mean shift: (1) adaptively estimating the scale of the object and (2) handling occlusions. The log likelihood function is used to extract object pixels and estimate the scale of the object. The Extreme learning machine is applied to train the radial basis function neural network to search for the object in case of occlusion or local convergence of mean shift. The experimental results show that the proposed algorithm can handle occlusion and estimate object scale effectively with less computational load making it suitable for real-time implementation.

Cite This Paper

Ramaravind K M, Shravan T R, Omkar S N,"Scale Adaptive Object Tracker with Occlusion Handling", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.1, pp.27-35, 2016. DOI: 10.5815/ijigsp.2016.01.03

Reference

[1]Yilmaz A., Javed O., and Shah M. "Object Tracking: a Survey", ACM Computing Surveys, 2006, 38, (4), Article 13, pp.2-5. 

[2]NM Artner, "A comparison of mean shift tracking methods", 12th Central European Seminar on computer graphics, 2008, pp.1-3.

[3]B Karasulu, "Review and evaluation of well-known methods for moving object detection and tracking", Journal of aeronautics and space technology, 2010, pp.1-4, 6-9.

[4]Fukunaga F. and Hostetler L. D. "The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition", IEEE Trans. on Information Theory, 1975, 21, (1), pp.32-40.

[5]Bradski G. "Computer Vision Face Tracking for Use in a Perceptual User Interface", Intel Technology Journal, 1998, pp.4-7.

[6]J Ning, L Zhang, D Zhang, C Wu, "Scale and orientation adaptive mean shift tracking", IET Computer Vision, 2012, pp.6-9.

[7]G.-B. Huang, Q. Y. Zhu, and C. K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, 2006, pp.3-9.

[8]RV Babu, S Suresh, A Makur, "Robust object tracking with radial basis function networks", ICASS, IEEE international Conference, 2007, pp.2-3.

[9]G.-B. Huang and C.-K. Siew, "Extreme learning machine with randomly assigned rbf kernels," International Journal of Information Technology, vol. 11, no. 1, pp. 16–24, 200. 

[10]Comaniciu D., Ramesh V., and Meer P. "Real-Time Tracking of Non-Rigid Objects Using Mean Shift". Proc. IEEE Conf. Computer Vision and Pattern Recognition, Hilton Head, SC, USA, June, 2000, pp. 142-149.

[11]J Ning, L Zhang , C Wu, "Robust mean-shift tracking with corrected background weighted histogram", IET Computer Vision, 2012, pp.4-6, 8-11.

[12]Comaniciu D., Ramesh V. and Meer P. "Kernel-Based Object Tracking", IEEE Trans. Pattern, Anal. Machine Intell., 2003, 25, (2), pp. 564-577.

[13]F Schwenker, HA Kestler, G Palm , "Three learning phases for radial basis function networks", Neural network, 2001, pp.4-8.

[14]"Radial Basis Function Network (RBFN) tutorial", http://chrisjmccormick.wordpress.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/, accessed Aug 2013.