This document summarizes a student's term project on lane identification in autonomous vehicles. The project pipeline involves camera calibration, perspective transformation, color and gradient thresholding to identify lane lines, and lane detection by fitting windows to identified lines. Results showed good performance on straight lanes but difficulty fitting curved lanes. Further work proposed includes providing distance to lane center, improving curved lane detection, and implementing lane identification in video.

1. Lane identification in the autonomous vehicle
Presenter: Aydin Ayanzadeh
Email:ayanzadeh17@itu.edu.tr
StudentID: 504161503
Term project of image processing

2. Agenda
● INTRODUCTION
● Pipeline of the Project
● Experiments and Results
● Conclusion
● what will the next?
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3. Challenging images
❖ Areas of low lighting
❖ Areas of brightness
❖ Areas of obscured lane lines
❖ Areas of rapid curvature changes
❖ High Reflections from Windshield
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4. Project pipeline
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5. Camera calibration
I. Camera architecture
Camera perspective
I. Distortion Correction
● Undistort camera image and
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6. Project pipeline
● Perspective transformation
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7. Project pipeline
● Color and gradient thresholds
● Apply Sobel filters to extract line/edges
● Convert to HSV color space and apply
color mask to identify yellow lines
● Combine binary masks
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8. Lane detection
● Combine the color mask and sobel
edge detector
● Peak point in the histogram
● Slide the windows horizontally
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9. Lane detection
● Good performance in straight
line
● Can not fit to the curved
road(polynomial regression has
not implemented)
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10. 10

11. Further works
● Give the distance of of vehicle from the center of
the line(suggest the side)
● Implement the lane detection for curved lane
● Using smooth kernel such as
● Implemented lane identification in the video
● Weather detection based on the illuminous of the
weather(I am not sure about this)
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12. References
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<http://www.springerlink.com/content/p5d9xp9gfkex5gk9>.
[2] Albiol, A., Monzo, D., Martin, A., Sastre, J., Albiol, A., 2008. Face recognition using HOG-EBGM. Pattern Recognition Lett. 29 (10), 1537–1543.
[3] Amin, M.A., Yan, H., 2009. An empirical study on the characteristics of gaborrepresentations for face recognition. IJPRAI 23 (3), 401–431.
[4] Baranda, J., Jeanne, V., Braspenning, R., 2008. Efficiency improvement of human body detection with histograms of oriented gradients. In: Proc. ICDSC08, pp. 1–9.
[5]Bartlett, M.S., Movellan, J.R., Sejnowski, T.J., 2002. Face recognition by independent component analysis. IEEE Trans. Neural Networks 13 (6), 1450–1464
<http://dx.doi.org/10.1109/TNN.2002.804287>.
[6]Bertozzi, M., Broggi, A., Rose, M.D., Felisa, M., Rakotomamonjy, A., Suard, F., 2007. A pedestrian detector using histograms of oriented gradients and a support vector machine
classifier. In: Proc. Intelligent Transportation Systems Conf., pp. 143– 148.
[7]Beveridge, J., Bolme, D., Draper, B., Teixeira, M., 2005. The CSU face identification evaluation system: Its purpose, features, and structure. MVA 16 (2), 128–138. Chellappa, R.,
Wilson, C., Sirohey, S., 1995. Human and machine recognition of faces: A survey. Proc. IEEE 83 (5), 705–740.
[8] Chellappa, R., Zhao, W. (Eds.), 2005. Face Processing: Advanced Modeling and Methods. Elsevier.
Chuang, C., Huang, S., Fu, L., Hsiao, P., 2008. Monocular multi-human detection using augmented histograms of oriented gradients. In: Proc. ICPR08, pp. 1–4.
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