A Multi-structure Elements Based Lane Recognition Algorithm

• Autorzy: Shen Y. , Dang J. , Ren E. , Lei T.

Warianty tytułu

PL

Zastosowanie modelowania multi-strukturalnego w algorytmie rozpoznawania linii

• Języki publikacji: EN

Abstrakty

EN

The existing traffic lane recognition algorithms have the weaknesses of low recognition ratio, bad robustness and real-time, for overcoming these drawbacks, this paper proposed an algorithm of lane recognition based on multi-structure elements model of morphological. In the algorithm, the region of interest (ROI) is extracted from the original image, which is detected by the operator of Canny. After that, the lanes are extracted by the structure elements, which have similar characteristics to that of lane model. Several lines are detected by Hough transformation, and choose the parameters to reconstruct the traffic lane. The experiment results show that this algorithm is simple, has better robustness, and at the same time, can efficiently detect the lane mask accurately and quickly.

PL

W artykule opisano metodę rozpoznawania linii na jezdni z poruszającego się pojazdu. Metoda bazuje na opracowaniu modelu morfologicznego multi-strukturalnego linii na podstawie danego obrazu oraz zastosowaniu transformaty Hough w celu wyznaczenia parametrów. Uzyskano w ten sposób zwiększenie szybkości w przypadku złożonego otoczenia, przy jednoczesnej niskim stopniu skomplikowania.

Słowa kluczowe

EN

lane detection; multi-structure; Region of Interest (ROI)

PL

modelowanie strukturalne; obszar ROI; algorytm rozpoznawania linii

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 4
• Strony: 206--210
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Shen Y.

• School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China, 730070

autor

Dang J.

• School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China, 730070

autor

Ren E.

• School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China, 730070

autor

Lei T.

• School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China, 730070

Bibliografia

• [1] Y.J. Fang, I. Masaki, B. Horn. Depth-Based Target Segmentation for Intelligent Vehicles: Fusion of Radar and Binocular Stereo [J]. IEEE Transactions on Intelligent Transportation Systems, 2002, 3(3): 196~202.
• [2] Q. Li, N. N. Zheng, et al. A Prototype Autonomous Vehicle and Its Algorithms for Lane Detection [J]. IEEE Transactions on Intelligent Transportation Systems, 2004,5(4): 300 ~308.
• [3] Z.W. Kim. Robust Lane Detection and Tracking in Challenging Scenarios [J]. IEEE Transactions on Intelligent Transportation Systems, 2008, 9(1): 16~26.
• [4] R. Danescu and S. Nedevschi, Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision [J]. IEEE Transactions on Intelligent Transportation Systems, 2009, 10(2):272 ~282.
• [5] R. Gregor, M. Lutzeler, et al. A Perceptual System for Autonomous Vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2002, 3 (1):49~59.
• [6] P. Jeong, S. Nedevschi. Efficient and Robust Classification Method Using Combined Feature Vector for Lane Detection [J]. IEEE Transaction on Circuits and Systems for Video Technology, 2005, 15(4):528~537.
• [7] H.Y. Cheng, B.S. Jeng, et al. Environment Classification and Hierarchical Lane Detection for Structured and Unstructured Roads [J]. IET Computer Vision, 2010，4(1): 37~49.
• [8] R.K. Satzoda, S. Sathyanarayana, T. Srikanthan. Hierarchical Additive Hough Transform for Lane Detection [J]. IEEE Embedded Systems Letters, 2010, 2(2):23~26.
• [9] Chunyou Xy, Rongben Wang, Keqiang Li. An road recognition algorithm based on straight line model [J]. Journal of image and graphics, 2004, 9(7):858-864.
• [10] Tao Lei, Yangyu Fang, Xiaopeng Wang. Lane detection algorithm based on morphological structure-elements model. Journal of Computer Applications. 2009, 29(2): 440~443.
• [11] Henk J.A. M. Heijmans. Composing Morphological Filters [J]. IEEE Transactions on Image Processing , 1997, 6( 5):713~723.
• [12] H. Park and J. Yoo. Structuring element decomposition for efficient implementation of morphological filters [J]. IEEE Proceedings- Vision Image and Signal Processing, 2001, 148 (1):31~35.
• [13] P. Soille. Morphological Image Analysis: Principles and Applications, 2 edition, New York, Springer-Verlag, USA, 2003.