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An efficient approach for highway lane detection based on the Hough transform and Kalman filter

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Abstract

The advances in lane detection technologies and computer vision enabled the evolution of lane-keeping systems, driver assistance and lane departure warning in traffic management for road safety. However, it is very challenging to identify and track the lane lines due to improper marking of lane lines and blind turns on the road. The present work proposes effective and efficient vision-based real-time lane markings and tracking lane detection methods for straight and curved lane lines. That can adapt to various environmental conditions. Further, the Hough transform optimization is performed to identify lane lines accurately, and the Kalman filter is employed to track lane lines detected in the ROI by the Sobel operator. The proposed approaches show their significance by achieving real-time response and high accuracy for a vehicle in lane change assistant system on highways. While comparing, the proposed methods show better results in terms of detection rate and processing time for straight lanes and detection accuracy, precision, recall and F1-Score for the curved lanes. The result of processing time and accuracy rate for straight lane detection is 16.7 fps, 96.3%, respectively, and the accuracy, precision, recall and F1-scores for curved lane detection are 97.74%, 98.15%, 97.35% and 97.75% in videos, respectively.

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Abbreviations

RGB:

Red Green Blue

HSV:

Hue Saturation Value

HSL:

Hue, Saturation and Lightness

\(dst \left( {x,y} \right)\) :

The pixel coordinate for the transformed image

\(src\;\left( {x,y} \right)\) :

The pixels in the input image

\(P_{k|k - 1}\) :

The predicted state covariance

\(F_{k - 1}\) :

State transition matrix

\(H_{k}\) :

Measurement transition matrix

\(G_{x}\) :

The gain of the Kalman filter

\(x_{k}^{ - }\) :

Posteriori estimate state

\(P_{k}\) :

Covariance matrix of a posteriori estimate error

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Authors and Affiliations

  1. Department of Computer Science, Banasthali Vidyapith, Banasthali, Rajasthan, India

    Sunil Kumar & Manisha Jailia

  2. Department of Computer Science & Engineering, School of Engineering & Technology, Sharda University, Greater Noida, India

    Sudeep Varshney

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  1. Sunil Kumar
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  2. Manisha Jailia
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Correspondence to Sunil Kumar.

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Kumar, S., Jailia, M. & Varshney, S. An efficient approach for highway lane detection based on the Hough transform and Kalman filter. Innov. Infrastruct. Solut. 7, 290 (2022). https://doi.org/10.1007/s41062-022-00887-9

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