Abstract
The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although it is important to adapt the mean-shift kernel to handle changes in illumination for robot vision at outdoor site, there is presently no clean mechanism for doing this. This paper presents a novel approach for color tracking that is robust to illumination changes for robot vision. We use two interleaved mean-shift procedures to track the spatial location and illumination intensity of a blob in an image. We demonstrate that our method enables efficient real-time tracking of the multiple color blobs against changes in illumination, where the illuminace ranges from 58 to 1,300 lx.
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Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21, 32–40 (1975)
Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1197–1203 (1999)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 564–577 (2003)
Collins, R.: Mean-shift blob tracking through scale space. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 234–240 (2003)
She, K., Bebis, G., Gu, H., Miller, R.: Vehicle tracking using on-line fusion of color and shape features. In: IEEE Conference on Intelligent Transportation Systems, pp. 731–736 (2004)
Marimont, D.H., Wandell, B.A.: Linear models of surface and illuminant spectra. Journal of the Optical Society of America 9(11), 1905–1913 (1992)
Drew, M.S., Wei, J., Li, Z.-N.: Illumination-invariant image retrieval and video segmentation. Pattern Recognition 32, 1369–1388 (1999)
Miller, E.G., Tieu, K.: Color eigenflows: Statistical modeling of joint color changes. In: IEEE International Conference on Computer Vision, vol. 1, pp. 607–614 (2001)
Bruce, J., Balch, T., Veloso, M.: Fast and inexpensive color image segmentation for interactive robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2061–2066 (2000)
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Hayashi, Y., Fujiyoshi, H. (2008). Mean-Shift-Based Color Tracking in Illuminance Change. In: Visser, U., Ribeiro, F., Ohashi, T., Dellaert, F. (eds) RoboCup 2007: Robot Soccer World Cup XI. RoboCup 2007. Lecture Notes in Computer Science(), vol 5001. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68847-1_29
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DOI: https://doi.org/10.1007/978-3-540-68847-1_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68846-4
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