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Lane detection and tracking based on annealed particle filter

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  • Robotics and Automation
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Abstract

This paper describes a highway lane detection and tracking algorithm based on an annealed particle filter; the algorithm combines multiple cues of road images with the annealed particle filter. We build a geometric lane model that can be applied to not only linear roads but also to curved roads. As a first step, preprocessing with a bar filter and color cues is used. In the annealed particle filter step, a K-means algorithm is utilized to measure the weights of the particles. We realize lane detection and tracking using the annealed particle filter which is the main contribution of the current paper. Experimental results show that our method is effective in various road situations. What’s more, the particle number and time cost of the annealed particle filter for each frame is largely reduced compared with those values when using the conventional particle filter.

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Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, Busan, Korea

    Hongying Zhao, Onecue Kim & Dong-Joong Kang

  2. Department of Mechanical & Automotive Engineering, Jeonju University, Jeonju, Korea

    Jong-Seob Won

Authors
  1. Hongying Zhao
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  2. Onecue Kim
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  3. Jong-Seob Won
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  4. Dong-Joong Kang
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Corresponding authors

Correspondence to Jong-Seob Won or Dong-Joong Kang.

Additional information

Recommended by Associate Editor Kang-Hyun Jo under the direction of Editor Myotaeg Lim.

We would like to acknowledge the financial support from the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2011-0017228 and 2013R1A1A206 0427).

Hongying Zhao received her BS degree in Automation from Dalian University of Technology, China in 2011. She is now a graduate student in mechanical engineering of Pusan National University, Korea. Her interests include machine vision, image processing, and robots.

Onecue Kim received his B.S. degree in Computer Science from Tongmyong University, Korea in 2008. He is now a student of successive postgraduate and doctoral programs of study in Mechanical Engineering of Pusan National University, Korea. His current research interests are visual surveillance, machine vision, robots and pattern recognition.

Jong-Seob Won received his Ph.D. degree in Mechanical Engineering from Texas A&M University, USA, in 2003. He is now a professor in Mechanical & Automotive Engineering of Jeonju University, Jeonju, Korea. His current research interests include Optimal control, Hybrid vehicle, and Intelligent controller design and its application to vehicles.

Dong-Joong Kang received his Ph.D. degree in Automation and Design Engineering at KAIST (Korea Advanced Institute of Science and Technology) in 1999. He is now an associate professor in Mechanical Engineering of Pusan National University. His current research interests include pattern recognition, mobile robot, visual inspection of factory products. He is a member of the IEEE and ICROS.

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Zhao, H., Kim, O., Won, JS. et al. Lane detection and tracking based on annealed particle filter. Int. J. Control Autom. Syst. 12, 1303–1312 (2014). https://doi.org/10.1007/s12555-013-0279-2

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  • DOI: https://doi.org/10.1007/s12555-013-0279-2

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