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Lane Detection by Dynamic Origin Technique for Advanced Driver Assistance System

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Lane detection is one of the key steps for developing driver assistance and vehicle automation features. A number of techniques are available for lane detection as part of computer vision tools to perform lane detection with different levels of accuracies. In this paper a unique method has been proposed for lane detection based on dynamic origin (DOT). This method provides better flexibility to adjust the outcome as per the specific needs of the intended application compared to other techniques. As the method offers better degree of control during the lane detection process, it can be adapted to detect lanes in varied situations like poor lighting or low quality road markings. Moreover, the Piecewise Linear Stretching Function (PLSF) has also been incorporated into the proposed method to improve the contrast of the input image source. Adding the PLSF method to the proposed lane detection technique, has significantly improved the accuracy of lane detection when compared to Hough transform method from 87.88% to 98.25% in day light situations and from 94.15% to 97% in low light situations.
Rocznik
Strony
589--594
Opis fizyczny
Bibliogr. 31 poz., fot., schem., tab., wykr.
Twórcy
autor
  • B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
autor
  • B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
Bibliografia
  • [1] V. Gaikwad, S. Lokhande, “Lane departure identification for advanced driver assistance,” IEEE Transactions on Intelligent Transportation Systems., 2015, 16(2): 910–918.
  • [2] Sandipann P. Narote, Pradnya N. Bhujbal, Abbhilasha S. Narote, Dhiraj M. Dhane, “A review of recent advances in lane detection and departure warning,” System. Pattern Recognition, 2018, 73: 216-234.
  • [3] P.C. Wu, C. Chang, C.H. Lin, “Lane mark extraction for automobiles under complex conditions,” Pattern Recognition, 2014, 47: 2756–2767.
  • [4] C. Mu, X. Ma, “Lane detection based on object segmentation and piecewise fitting,” Telkomnika Indonesian Journal of Electrical Engineering, 2014, 12(5): 3491–3500.
  • [5] CALTECH database http://www.vision.caltech.edu/archive.html
  • [6] Y. Dong, J. Xiong, L. Li, J. Yang “Lane detection based on object segmentation and piecewise fitting,” ICCP proceedings, 2012, 461–464.
  • [7] P. Hsiao, C.W. Yeh, S. Huang, L.C. Fu, “Portable vision based real time lane departure warning system day and night,” IEEE Transactions on Vehicular Technology, 2009, 58(4): 2089–2094.
  • [8] Prashanth Viswanath, Pramod Swami, “A Robust and Real-Time Image Based Lane Departure Warning System,” IEEE International Conference on Consumer Electronics, 2016.
  • [9] Minghua Niu, Jianmin Zhang, Gen Li, “Research on the Algorithms of Lane Recognition based on Machine Vision,” International Journal of Intelligent Engineering and Systems, 2015, 8(4).
  • [10] Gulivindala Suresh, Chanamallu Srinivasa Rao, “Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features,” International Journal of Intelligent Engineering and Systems, 2019, 12(2).
  • [11] C.R. Jung, C.R. Kelber, “Lane following and lane departure using a linear parabolic mode,” Image and Vision Computing, 2005, 23(13): 1192–1202.
  • [12] D. Kragic, L. Petersson and H.I. Christensen, “Visually guided manipulation tasks,” Robotics and Autonomous Systems, 2002, 40(2/3): 193-203.
  • [13] J.W. Lee, “A machine vision system for lane departure detection. Computing,” Vision Image Understanding, 2002, 86(1): 52–78.
  • [14] J. Melo, A. Naftel, A. Bernardino, J. Santos, “Detection and classification of highway lanes using vehicle motion trajectories,” IEEE Transactions on Intelligent Transportation Systems, 2006, 7(2): 188–200.
  • [15] Chaiwat Nuthong; Theekapun Charoenpong, “Lane detection using smoothing,” 3rd International Congress on Image and Signal Processing, 2010, 989-993.
  • [16] Bing Yu; Weigong Zhang; Yingfeng Cai, “A Lane Departure Warning System Based on Machine Vision,” Proceeding IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008, 197-201.
  • [17] J.G. Wang, C. Lin, S. Chen, “Applying fuzzy method to vision-based lane detection and departure warning system,” Expert Systems with Applications, 2010, 3(1): 113–126.
  • [18] H. Xu, H. Li, “Study on a robust approach of lane departure warning algorithm,” IEEE International Conference on Signal Processing System (ICSPS), 2010, 201–204.
  • [19] S. Srivastava, M. Lumb, R. Singal, “Improved Lane Detection using Hybrid Median Filter and Modified Hough Transform,” International Journal of Advanced Research in Computer Science and Software Engineering, 2014, 4(1): 30–37.
  • [20] H. Aung, M.H. Zaw, “Video based lane departure warning system using Hough transform,” International Conference on Advances in Engineering and Technology (ICAET), 2010, 85–88.
  • [21] X. An, E. Shang, J. Song, J. Li, H. He, “Real-time lane departure warning system based on a single FPGA,” Eurasip Journal on Image and Video Processing, 2013, 38(1–18).
  • [22] J. Son, H. Yoo, S. Kim, K. Sohn, “Real-time illumination invariant lane detection for lane departure warning system,” Expert Systems with Applications, 2015, 42(4): 1816–1824.
  • [23] Y. Wang, D. Shen, E.K. Teoh, “Lane detection using spline model,” Pattern Recognition, 2000, 21(9): 677–689.
  • [24] C.J. Lin, J.G. Wang, S.M. Chen, C.Y. Lee, “Design of a lane detection and departure warning system using functional link-based neuro-fuzzy network,” IEEE International Conference on Fuzzy System (FUZZ), 2010, 1–7.
  • [25] Q. Lin, Y. Han, H. Hahn, “Real time lane detection based on extended edge-linking algorithm,” IEEE International Conference on Computer Research and Development, 2010, 725–730.
  • [26] C. Tu, B.V. Wyk, Y. Hamam, K. Djouni, S. Du, “Vehicle Position Monitoring using,” Hough Transform. IERI Procedia, 2013; 4: 316–322.
  • [27] E. Salari, D. Ouyang, “Camera-based forward collision and lane departure warning system using svm,” IEEE 56th International Midwest Symp. On Circuits and Systems (MWSCAS), 2013, 1278–1281.
  • [28] A.S. Aguadoa, Eugenia, Montie and M. S. Nixonc, “Invariant characterisation of the Hough transform for pose estimation of arbitrary shapes,” Pattern Recognition, 2002, 35(5): 1083-1097.
  • [29] Borkar, M. Hayes, M. Smith, “Robust lane detection and tracking with Ransac and Kalman filter,” 16th IEEE International Conference on Image Processing (ICIP), 2009, 3261–3264.
  • [30] N. Madrid, P. Hurtik “Lane departure warning for mobile devices based on a fuzzy representation of images,” Fuzzy Sets System, 2016, 291: 144–159.
  • [31] P. Maya, C. Tharini, “Performance Analysis of Lane Detection Algorithm using Partial Hough Transform,” 21st International Arab Conference on Information Technology (ACIT'2020), 2020, Egypt.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a4aa09fc-ae4f-44c3-ba54-fb37fdf64f6d
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