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Autonomous Intelligent Vehicles pp 33–59Cite as

Road Detection and Tracking

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Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

In this chapter, we introduce the state-of-the-art of road detection and tracking which are important tasks in intelligent transportation systems and intelligent vehicle applications. We review the related work on road detection and tracking first. Moreover, we introduce two types of road recognizing approaches, one is for structured roads, and the other is for unstructured roads. For structured road, we build a lane shape model and then present a new Adaptive Random Hough Transform (ARHT) to detect the lane, which combines the advantages of both the AHT and RHT. The experiment results show that this approach is robust and efficient for structured lane detection and has some advantages over a genetic algorithm. For unstructured roads, we use Mean Shift (MS) algorithms to recognize road and non-road areas based on color and texture features. Also we introduce the basic mean shift algorithm and its applications. Based on particle filtering, we formulate lane tracking to be the estimation of lane’s parameters and vehicle’s state. We present a particle filtering algorithm and build lane and dynamic system models. Then the CONDENSATION algorithm is used to estimate the shape of the road ahead of the vehicle.

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Notes

  1. 1.

    http://www.smarteye.se.

  2. 2.

    http://vasc.ri.cmu.edu/idb/html/road/index.html.

  3. 3.

    http://www.vision.caltech.edu/bouguetj/calib_doc/.

  4. 4.

    http://vasc.ri.cmu.edu/idb/html/road/index.html.

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Authors and Affiliations

  1. School of Automation Engineering, University of Electronic Science and Technology, 610054, Chengdu, Sichuan, People’s Republic of China

    Prof. Hong Cheng

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  1. Prof. Hong Cheng
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Correspondence to Hong Cheng .

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© 2011 Springer-Verlag London Limited

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Cheng, H. (2011). Road Detection and Tracking. In: Autonomous Intelligent Vehicles. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2280-7_4

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  • DOI: https://doi.org/10.1007/978-1-4471-2280-7_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2279-1

  • Online ISBN: 978-1-4471-2280-7

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