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http://dx.doi.org/10.14400/JDC.2015.13.9.183

Real-Time Object Tracking Algorithm based on Adaptive Color Model in Surveillance Networks  

Kang, Sung-Kwan (Dept. of Computer and Information Engineering, Inha University)
Lee, Jung-Hyun (Dept. of Computer and Information Engineering, Inha University)
Publication Information
Journal of Digital Convergence / v.13, no.9, 2015 , pp. 183-189 More about this Journal
Abstract
In this paper, we propose an object tracking method using the color information of the image in surveillance network. This method perform a object detection using of adaptive color model. Object contour detection plays an important role in application such as object recognition. Experimental results demonstrate successful object detection over a wide range of object's variation in color and scale. In applications to detect an object in real time, when transmitting a large amount of image data it is possible to find the mode of a color distribution. The specific color of an object is modified at dynamically changing color in image. So, this algorithm detects the tracking area information of object within relevant tracking area and only tracking the movement of that object.Through experiments, we show that proposed method is more robust than other methods under certain ideal situations.
Keywords
Object Detection; Skin Color-based Tracking; Color Segmentation; Object Tracking; Surveillance Networks;
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1 W. Zhang and G. Cao, Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks, IEEE Transactions on Wireless Communications, Vol. 3, No. 5, September 2004.
2 D. R. Kincaid and W. W. Cheney, Numerical Analysis: the Mathematics of Scientific Computing, Van Nostrand, 1991.
3 S. M. LaValle, Planning Algorithms, Cambridge University Press, 2006.
4 Wang and X. Tang, A Unified Framework for Subspace Object Recognition, IEEE Trans. on PAMI, Vol. 26, No. 9, pp. 1222-1228, 2004.   DOI   ScienceOn
5 S. J. Maybank, A. D. Worrall and G. D. Sullivan, Filter for Car Tracking Based on Acceleration and Steering Angle, British Machine Vision Conference, 1996.
6 C. W. Ng and S. Ranganath, "Real-time Gesture Recognition System and Application," Image and Vision Computing, Vol. 20, Issues 13-14, pp. 993-1007, 2002.   DOI   ScienceOn
7 D. H. Liu, K. M. Lam, and L. S. Shen, "Illumination invariant object recognition" Journal of Pattern Recognition, Vol.38, pp.1705-1716, 2005.   DOI   ScienceOn
8 H. Schneiderman and T. Kanade, "Object Detection Using the Statistics of Parts," Int'l J. Computer Vision, Vol. 56, No. 3, pp. 151-177, 2004.   DOI
9 Hyun-Chul Kim; Daijin Kim; Sung Yang Bang; "Face recognition using LDA mixture model," Pattern Recognition, 2002. Proceedings. 16th International Conference on , 11-15, 8. 2002. pp: 486-489 vol.2
10 R. Duda, P. Hart, and D. Stork, "Pattern Classification," Second Edition, John Willey & Sons Publications, New York, 2001.
11 P. Phillips, "The FERET Database and Evolution Procedure for Object Recognition Al-gorithms," Image and Vision Computing, Vol. 16, No. 5, pp. 295-306, 1999.   DOI
12 J. W. Ko, K. Y. Chung, J. S. Han, "Model Transformation Verification using Similarity and Graph Comparison Algorithm", Multimedia Tools and Applications, 2013. Doi: 10.1007/s11042-013-1581-y   DOI
13 Y. Ko, V. Shankarkumar and N. H. Vaidya, Medium Access Control Protocols Using Directional Antennas in Ad Hoc Networks, IEEE Infocom, March 1999.
14 A. Aljadhai and T. F. Znati, Predictive Mobility Support for QoS Provisioning in Mobile Wireless Environments, IEEE Journal on Selected Areas in Communications (JSAC), Vol. 19, No. 10, October 2001.
15 V. Kawadia and P. R. Kumar, Power Control and Clustering in Ad Hoc Networks, IEEE Infocom, March 2003.