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Road Detection and Tracking

  • Hong Cheng
Part of the Advances in Computer Vision and Pattern Recognition book series (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.

Keywords

Vision Sensor Dynamic System Model Hough Transform Shift Algorithm Lane Marking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.School of Automation EngineeringUniversity of Electronic Science and TechnologyChengduPeople’s Republic of China

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