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Issue No.04 - July-Aug. (2012 vol.27)
pp: 57-62
Chunzhao Guo , Toyota Central R&D Laboratories
Seiichi Mita , Toyota Technological Institute
This article presents a semantic graph representation for vision-based intelligent vehicle systems. It can represent the traffic scene with both perceptional meaning of object classes and the spatial relations between them. Using such graphs offers superior performance in terms of both accuracy and robustness. Furthermore, a stereovision-based road boundary estimation system, designed for navigating an intelligent vehicle through challenging traffic scenarios, is introduced, which exemplifies the advantages of the semantic graph.
Road vehicles, Semantics, Image edge detection, Intelligent vehicles, Image segmentation, Stereo vision, Hidden Markov models, roadmap, intelligent vehicles, semantic graph, stereo vision
Chunzhao Guo, Seiichi Mita, "A Semantic Graph of Traffic Scenes for Intelligent Vehicle Systems", IEEE Intelligent Systems, vol.27, no. 4, pp. 57-62, July-Aug. 2012, doi:10.1109/MIS.2012.65
1. N. Zheng et al., “, Toward Intelligent Driver-Assistance and Safety Warning System,” IEEE Intelligent Systems, vol. 19, no. 2, 2004, pp. 8–11.
2. M. Buehler, K, Lagnemma, and S. Singh eds., “The DARPA Urban Challenge: Autonomous Vehicles in City Traffic,” Springer Tracts in Advanced Robotics, vol. 56, 2010.
3. H. Badino, U. Franke, and R. Mester, “Free Space Computation Using Stochastic Occupancy Grids and Dynamic Programming,” Workshop on Dynamical Vision, Int'l Conf. Computer Vision, 2007; .
4. T. Xia, M. Yang, and R. Yang, “CyberC3: A Prototype Cybernetic Transportation System for Urban Applications,” IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 1, 2010, pp. 142–152.
5. P. Felzenszwalb and D. Huttenlocher, “Efficient Graph-Based Image Segmentation,” Int'l J. Computer Vision, vol. 59, no. 2, 2004; .
6. G.J. Brostow et al., “Segmentation and Recognition Using Structure from Motion Point Clouds,” Proc. European Conf. Computer Vision, Part I, 2008; eccv08.pdf.
7. F. Schroff, A. Criminisi, and A. Zisserman, “Object Class Segmentation Using Random Forests,” Proc. British Machine Vision Conf., 2008; criminisi_bmvc2008.pdf.
8. C. Wojek and B. Schiele, “A Dynamic CRF Model for Joint Labeling of Object and Scene Classes,” Proc. European Conf. Computer Vision, Part IV, 2008, pp. 733–747.
9. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge Univ. Press, 2000.
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