Abstract
This paper presents a technique for object tracking by using CAMSHIFT algorithm that tracks an object based on color. We aim to improve the CAMSHIFT algorithm by adding a multiple targets tracking function [1].When one object is selected as a template, then it will search objects that have the same hue value and shape by shape recognition. So,the inputs of the algorithm are hue values and shape of the object. When all objects are absent in the frame, the algorithm will search whole frame to find most similar-looking objects and track them. The important task of the object tracking is to separate a target from background or frame that in some cases where the noise is present in the tracking frame. Then object identification method was added to the algorithm for filtering the noise and counting numbers of objects to make decide how many targets track.
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Hidayatullah, P., Konik, H.: CAMSHIFT Improvement on Multi-Hue and Multi-Object Tracking. In: International Conference on Electrical Engineering and Informatics, Bandung, Indonesia (2011)
Gorry, B., Chen, Z., Hammond, K., Wallace, A., Michaelson, A.: Using Mean-Shift Tracking Algorithms for Real-Time Tracking of Moving Images on an Autonomous Vehicle Testbed Platform. World Academy of Science, Engineering and Technology (2007)
Amores, P.M.M., Gagwis, J., Jamora Marie, A.: PIC – Based Color And Shape Recognition: Using 3-Link Robotic Arm (2011)
Bradski, R.G.: Computer Vision Face Tracking For Use in a Perceptual User Interface. Microcomputer Research Lab. Santa Clara, CA. Intel Corporation (1998)
Exner, D., Bruns, E., Kurz, D., Grundhofer, A.: Fast and Robust CAMSHIFT Tracking. Bauhaus-University Weimar, Germany (2010)
Cognotics. How OpenCV’s Face Tracker Works (2007), http://www.cognotics.com
Mathwork. Remove small objects from binary image (2012), http://www.mathworks.com/help/images/ref/bwareaopen.html
Pomplun, M.: Compactness (2007), http://www.cs.umb.edu/~marc/cs675/cv09-11.pdf
Zhongwan, L.: Compactness theorem. Mathematical Logic for Computer Science 2, 147–158 (1998)
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© 2013 Springer-Verlag Berlin Heidelberg
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Sooksatra, S., Kondo, T. (2013). CAMSHIFT-Based Algorithm for Multiple Object Tracking. In: Meesad, P., Unger, H., Boonkrong, S. (eds) The 9th International Conference on Computing and InformationTechnology (IC2IT2013). Advances in Intelligent Systems and Computing, vol 209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37371-8_33
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DOI: https://doi.org/10.1007/978-3-642-37371-8_33
Publisher Name: Springer, Berlin, Heidelberg
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