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Intelligent Vision Systems for Landmark-Based Vehicle Navigation

Intelligent Vision Systems for Landmark-Based Vehicle Navigation

Wen Wu, Jie Yang, Xilin Chen
Copyright: © 2011 |Pages: 19
ISBN13: 9781609600242|ISBN10: 160960024X|EISBN13: 9781609600266
DOI: 10.4018/978-1-60960-024-2.ch001
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MLA

Wu, Wen, et al. "Intelligent Vision Systems for Landmark-Based Vehicle Navigation." Computer Vision for Multimedia Applications: Methods and Solutions, edited by Jinjun Wang, et al., IGI Global, 2011, pp. 1-19. https://doi.org/10.4018/978-1-60960-024-2.ch001

APA

Wu, W., Yang, J., & Chen, X. (2011). Intelligent Vision Systems for Landmark-Based Vehicle Navigation. In J. Wang, J. Cheng, & S. Jiang (Eds.), Computer Vision for Multimedia Applications: Methods and Solutions (pp. 1-19). IGI Global. https://doi.org/10.4018/978-1-60960-024-2.ch001

Chicago

Wu, Wen, Jie Yang, and Xilin Chen. "Intelligent Vision Systems for Landmark-Based Vehicle Navigation." In Computer Vision for Multimedia Applications: Methods and Solutions, edited by Jinjun Wang, Jian Cheng, and Shuqiang Jiang, 1-19. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-024-2.ch001

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Abstract

Human drivers often use landmarks for navigation. For example, we tell people to turn left after the second traffic light and to make a right at Starbucks. In our daily life, a landmark can be anything that is easily recognizable and used for giving navigation directions, such as a sign or a building. It has been proposed that current navigation systems can be made more effective and safer by incorporating landmarks as key navigation cues. Especially, landmarks support navigation in unfamiliar environments. In this chapter, we aim to describe technologies for two intelligent vision systems for landmark-based car navigation: (1) labeling street landmarks in images with minimal human effort; we have proposed a semi-supervised learning framework for the task; (2) automatically detecting text on road signs from video; the proposed framework takes advantage of spatio-temporal information in video and fuses partial information for detecting text from frame to frame.

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