Abstract
Correlation trackers are in use for the past four decades. Edge based correlation tracking algorithms have proved their strength for long term tracking, but these algorithms suffer from two major problems: clutter and slow occlusion. Thus, there is a requirement to improve the confidence measure regarding target and non-target object. In order to solve these problems, we present an “Edge Enhanced Fragment Based Normalized Correlation (EEFNC)” algorithm, in which we: (1) divide the target template into nine non-overlapping fragments after edge-enhancement, (2) correlate each fragment with the corresponding fragment of the template-size section in the search region, and (3) achieve the final similarity measure by averaging the correlation values obtained for every fragment. A fragment level template updating method is also proposed to make the template adaptive to the variation in the shape and appearance of the object in motion. We provide the experimental results which show that the proposed technique outperforms the recent Edge-Enhanced Normalized Correlation (EENC) tracking algorithm in occlusion and clutter.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving Target Classification and Tracking from Real-time Video. In: IEEE Workshop on Applications of Computer Vision (1998)
Lucas, B., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: 7th International Joint Conference on Artificial Intelligence (IJCAI), pp. 674–679 (1981)
Wong, S.: Advanced Correlation Tracking of Objects in Cluttered Imagery. In: Proceedings of SPIE, vol. 5810 (2005)
Stauffer, C., Grimson, W.: Learning Patterns of Activity Using Real Time Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–767 (2002)
Ahmed, J., Jafri, M.N., Shah, M., Akbar, M.: Real-Time Edge-Enhanced Dynamic Correlation and Predic-tive Open-Loop Car Following Control for Robust Tracking. Machine Vision and Applications Journal 19(1), 1–25 (2008)
Kalman, R.E., Bucy, R.S.: New Results in Linear Filtering and Prediction Theory. Transactions of the ASME - Journal of Basic Engineering 83 (1961)
Adam, A., Rivlin, E., Shimshoni, I.: Robust Fragments-based Tracking using the Integral Histogram. In: IEEE Conference on Computer Vision and Pattern Recognition, June 17-22 (2006)
Caviar datasets, http://groups.inf.ed.ac.uk/vision/caviar/caviardata1/
Ahmed, J., Jafri, M.N., Ahmad, J., Khan, M.I.: Design and Implementation of a Neural Network for Real-Time Object Tracking. In: International Conference on Machine Vision and Pattern Recognition in Conjunction with 4th World Enformatika Conference, Istanbul (2005)
Porikli, F.: Integral histogram a fast way to extract histograms in cartesian spaces. In: IEEE Conference on Computer Vision and Pattern Recognition (2005)
Comaniciu, D., Visvanathan, R., Meer, P.: Kernel based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non rigid objects using mean shift. In: Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, vol. 1, pp. 142–149 (2000)
Lathoud, G., Odobez, J., Gatica-Perez, D.: AV16.3: An Audio-Visual Corpus for Speaker Localization and Tracking. In: Bengio, S., Bourlard, H. (eds.) MLMI 2004. LNCS, vol. 3361, pp. 182–195. Springer, Heidelberg (2005)
MATLAB 7.0 On-line Help Documentation
Ahmed, J., Jafri, M.N.: Best-Match Rectangle Adjustment Algorithm for Persistent and Precise Correlation Tracking. In: Proc. IEEE International Conference on Machine Vision, Islamabad, Pakistan, December 28-29 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khan, M.I., Ahmed, J., Ali, A., Masood, A. (2009). Robust Edge-Enhanced Fragment Based Normalized Correlation Tracking in Cluttered and Occluded Imagery. In: Ślęzak, D., Pal, S.K., Kang, BH., Gu, J., Kuroda, H., Kim, Th. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2009. Communications in Computer and Information Science, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10546-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-10546-3_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10545-6
Online ISBN: 978-3-642-10546-3
eBook Packages: Computer ScienceComputer Science (R0)