Commenced in January 2007
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Paper Count: 31473
Object Motion Tracking Based On Color Detection for Android Devices

Authors: Zacharenia I. Garofalaki, John T. Amorginos, John N. Ellinas


This paper presents the development of a robot car that can track the motion of an object by detecting its color through an Android device. The employed computer vision algorithm uses the OpenCV library, which is embedded into an Android application of a smartphone, for manipulating the captured image of the object. The captured image of the object is subjected to color conversion and is transformed to a binary image for further processing after color filtering. The desired object is clearly determined after removing pixel noise by applying image morphology operations and contour definition. Finally, the area and the center of the object are determined so that object’s motion to be tracked. The smartphone application has been placed on a robot car and transmits by Bluetooth to an Arduino assembly the motion directives so that to follow objects of a specified color. The experimental evaluation of the proposed algorithm shows reliable color detection and smooth tracking characteristics.

Keywords: Android, Arduino Uno, Image processing, Object motion detection, OpenCV library.

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[1] Tang Sze Ling et al, “Colour-based Object Tracking in Surveillance Application”, Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. I, Hong Kong, March 2009.
[2] J.F. Engelberger, “Health-care Robotics Goes Commercial: The Helpmate Experience”, Robotics, vol. 11, 1993, pp. 517-523.
[3] L. Davis, V. Philomin and R. Duraiswami, “Tracking Humans from a Moving Platform”, IEEE Computer Society Proceedings of the International Conference on Pattern Recognition, vol. 4, 2000, pp. 4171.
[4] O. Javed and M.S. Yilmaz, “Object Tracking: A survey”, ACM Journal of Computing Surveys, vol. 38, no. 4, article 13, 2006.
[5] D. Comanciu, P. Meer, “Mean shift: A robust approach toward feature space analysis”, IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 24, no. 5, 2002, pp. 603–619.
[6] D. Comanciu, V. Ramesh, P. Meer, “Kernel-based object tracking”, IEEE Transactions on Pattern Analysis Machine Intelligence, vol. 25, 2003, pp. 564–575.