12 April 2018 Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions
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Abstract
As one of the essential components of environment perception techniques for an intelligent vehicle, lane detection is confronted with challenges including robustness against the complicated disturbance and illumination, also adaptability to stochastic lane shapes. To overcome these issues, we proposed a robust lane detection method named classification-generation-growth-based (CGG) operator to the detected lines, whereby the linear lane markings are identified by synergizing multiple visual cues with the a priori knowledge and spatial–temporal information. According to the quality of linear lane fitting, the linear and linear-parabolic models are dynamically switched to describe the actual lane. The Kalman filter with adaptive noise covariance and the region of interests (ROI) tracking are applied to improve the robustness and efficiency. Experiments were conducted with images covering various challenging scenarios. The experimental results evaluate the effectiveness of the presented method for complicated disturbances, illumination, and stochastic lane shapes.
© 2018 SPIE and IS&T
Zhi Huang, Zhi Huang, Baozheng Fan, Baozheng Fan, Xiaolin Song, Xiaolin Song, } "Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions," Journal of Electronic Imaging 27(2), 023025 (12 April 2018). https://doi.org/10.1117/1.JEI.27.2.023025 . Submission: Received: 5 November 2017; Accepted: 19 March 2018
Received: 5 November 2017; Accepted: 19 March 2018; Published: 12 April 2018
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