Abstract
This paper presents an improved cross-correlation algorithm for template-based object tracking: the reduced spiral search with a sparse angular sampling. The basic parameters of the algorithm for the real-time face tracking were evaluated regarding their impact on the algorithm performance. They are the minimum number of pixels and the size of the template, the correlation threshold and drifting, and the parameters of the search – radius, shift, direction, and rotation of the template. We demonstrated that the information provided by the grid-like template might be reduced to 16 pixels with a grid step of 15 pixels. A spiral search in 8 directions with a minimum shift of 1 pixel decreases the number of computations by 20 times. Being activated sequentially the template rotation does not increase the performance, but doing the tracking adaptive and robust.
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References
Bérard, F.: The perceptual window: Head motion as a new input stream. In: IFIP Conference on Human-Computer Interaction, pp. 238–244 (1999)
Betke, M., Gips, J., Fleming, P.: The Camera Mouse: Visual Tracking of Body Features to Provide Computer Access For People with Severe Disabilities. IEEE Transactions on Neural Systems and Rehabilitation Engineering 10(1), 1–10 (2002)
Blalock, T.N., et al.: Method and Device for Tracking Relative Movement by Correlating Signals From an Array of Photoelements, US Patent 5729008 (1998)
Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Analysis and Machine Intelligence 15(10), 1042–1052 (1993)
Comaniciu, D., Ramesh, V., Meer, P.: Real-Time Tracking of Non-Rigid Objects using Mean Shift. In: IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2000), vol. 2, pp. 142–149 (2002)
Crowley, J.L., Berard, F., Coutaz, J.: Finger Tracking as an Input Device for Augmented Reality. In: Int. Workshop on Face and Gesture Recognition, pp. 195–200 (1995)
FaceMOUSE. Product information. Web site (2005), http://www.aidalabs.com/
Intel Image Processing library Open CV, http://www.intel.com/technology/~/opencv/
Jilin Tu, T., Huang, T., Tao, H.: Face As Mouse Through Visual Face Tracking. In: Proc. of the 2nd Canadian Conf Computer and Robot Vision, pp. 339–346 (2005)
Lewis, J.P.: Fast Template Matching. Vision Interface, 120–123 (1995)
Product information on EyeTwig.com. Website (2005), http://www.eyetwig.com
Zhang, S.-C., Liu, Z.-Q.: A robust, real-time ellipse detector. J. Pattern Recognition 38, 273–287 (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Evreinova, T.V., Evreinov, G., Raisamo, R. (2006). Video as Input: Spiral Search with the Sparse Angular Sampling. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds) Computer and Information Sciences – ISCIS 2006. ISCIS 2006. Lecture Notes in Computer Science, vol 4263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11902140_58
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DOI: https://doi.org/10.1007/11902140_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47242-1
Online ISBN: 978-3-540-47243-8
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