Abstract
Two-dimension road following is one of the crucial tasks of vision navigation. For the reasons of environment complexity and the discrepancy between motion images, the robust outdoor road following for two-dimension image sequence is still a challenging task. This paper proposes a novel road following method, which firstly uses the Mean Shift algorithm with embedded edge confidence to partition the images into homogenous regions with precise boundary. Then, according to the color statistic information of the road/non-road model obtained from previous frames, the Graph Cuts (GC) algorithm is used to achieve the final binary images and update the road/non-road model simultaneously. This algorithm combines the advantages of Graph Cuts algorithm and Mean Shift algorithm, and effectively solves some difficult problems of conventional methods, such as the adaptive selection of road model under complex environments, and the choice of effective criteria for the region merging. Experiment results indicate our method possesses excellent performance under complicated environment, and meets the requirements of fast computing.
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References
Kumar, M.P., Torr, P.H.S., Zisserman, A.: OBJ CUT. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 1, 18–25 (2005)
Kolmogorov, V., Criminisi, A., Blake, A.: Bi-Layer Segmentation of Binocular Stereo Video. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA, vol. 2, pp. 415–438 (2005)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 2(5), 603–619 (2002)
Comaniciu, D., Meer, P.: Mean Shift Analysis and Applications. In: Proceedings of the IEEE Internation Conference on Computer Vision, Kerkyra, Greece, vol. 1, pp. 1197–1203 (1999)
Allen, G.J., Richard, Y.D., Jesse, S.J.: Object Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces. In: Proceedings of the Pan-Sydney Area Workshop on Vision Information Processing, Sydney, Australian, vol. 36, pp. 3–7 (2004)
Kolmogorov, V., Zabih, R.: What Energy Functions can be Minimized via Graph Cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 147–159 (2004)
Fukunaga, K., Hostetler, L.O.: The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Transactions on Information Theory IT-21(1), 32–40 (1975)
Meer, P., Georgescu, P.: Edge detection with embedded confidence. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1351–1365 (2001)
Christoudias, C.M., Georgescu, B., Meer, P.: Synergism in low level vision. Proceedings of International Conference on Pattern Recognition 1, 150–155 (2002)
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Lin, L., Zhou, W. (2007). A Robust and Adaptive Road Following Algorithm for Video Image Sequence. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2007. Lecture Notes in Computer Science, vol 4681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74171-8_105
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DOI: https://doi.org/10.1007/978-3-540-74171-8_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74170-1
Online ISBN: 978-3-540-74171-8
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