1. Lane identification in the autonomous vehicles Presenter: Aydin Ayanzadeh Email:ayanzadeh17@itu.edu.trayanzadeh17@itu.edu.tr StudentID: 504161503 Term project of image processing

2. Agenda ● INTRODUCTION ●Pipeline of the Project ●Results ●Conclusion 2

3. Introduction - PROJECT DESCRIPTION 3

4. Challenging images ❖ Areas of low lighting ❖ Areas of brightness ❖ Areas of obscured lane lines ❖ Areas of rapid curvature changes ❖ High Reflections from Windshield 4

5. Project pipeline 5

6. Camera calibration I.Camera architecture Camera perspective I.Distortion Correction ●Undistort camera image and 6

7. Project pipeline ●Perspective transformation 7

8. Project pipeline ●Convert to HSV color space and apply ●In the next step, a HSV color mask is applied to detect the white and yellow lanes ●color mask to identify yellow lines ●Combine binary masks 8

9. Lane detection ●Peak point in the histogram ●Slide the windows horizontally and vertically ●Polynomial Fitting 9

10. Results ●Good performance in straight line ●Can not fit to the curved road(polynomial regression has not implemented) 10

11. Results 11

12. Results ●bad quality of the lines, Shadows of the tree from the sun ●sharper curves in the road have impact on the detection of the lane ❏ Suggestions ●Adaptive threshold ●Polynomial coefficient matrix ●Deep learning methods 12

13. References [1] Ahonen, T., Hadid, A., Pietikainen, M., 2004. Face recognition with local binary patterns. In: Proc. Eighth European Conf. Computer Vision, pp. 469–481.. [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. [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. 13

14. 14