A Learning Approach Towards Detection and Tracking of Lane Markings

Road scene analysis is a challenging problem that has applications in autonomous navigation of vehicles. An integral component of this system is the robust detection and tracking of lane markings. It is a hard problem primarily due to large appearance variations in lane markings caused by factors such as occlusion (traffic on the road), shadows (from objects like trees), and changing lighting conditions of the scene (transition from day to night). In this paper, we address these issues through a learning-based approach using visual inputs from a camera mounted in front of a vehicle. We propose the following: 1) a pixel-hierarchy feature descriptor to model the contextual information shared by lane markings with the surrounding road region; 2) a robust boosting algorithm to select relevant contextual features for detecting lane markings; and 3) particle filters to track the lane markings, without knowledge of vehicle speed, by assuming the lane markings to be static through the video sequence and then learning the possible road scene variations from the statistics of tracked model parameters. We investigate the effectiveness of our algorithm on challenging daylight and night-time road video sequences.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[3]  Gunnar Rätsch,et al.  An Introduction to Boosting and Leveraging , 2002, Machine Learning Summer School.

[4]  ZuWhan Kim,et al.  Robust Lane Detection and Tracking in Challenging Scenarios , 2008, IEEE Transactions on Intelligent Transportation Systems.

[5]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[6]  Ayhan Demiriz,et al.  Linear Programming Boosting via Column Generation , 2002, Machine Learning.

[7]  Luo Si,et al.  A New Boosting Algorithm Using Input-Dependent Regularizer , 2003, ICML 2003.

[8]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Gunnar Rätsch,et al.  Totally corrective boosting algorithms that maximize the margin , 2006, ICML.

[10]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[11]  David M. Bevly,et al.  A Low-Cost Solution for an Integrated Multisensor Lane Departure Warning System , 2009, IEEE Transactions on Intelligent Transportation Systems.

[12]  Rama Chellappa,et al.  Discriminant Analysis for Recognition of Human Face Images (Invited Paper) , 1997, AVBPA.

[13]  Nanning Zheng,et al.  Springrobot: a prototype autonomous vehicle and its algorithms for lane detection , 2004, IEEE Transactions on Intelligent Transportation Systems.

[14]  齋藤 三郎,et al.  Theory of reproducing kernels and its applications , 1988 .

[15]  S. Nedevschi,et al.  3D lane detection system based on stereovision , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[16]  Dong Joong Kang,et al.  Road lane segmentation using dynamic programming for active safety vehicles , 2003, Pattern Recognit. Lett..

[17]  Ernst D. Dickmanns,et al.  Recursive 3-D Road and Relative Ego-State Recognition , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Christopher M. Kreucher,et al.  LANA: a lane extraction algorithm that uses frequency domain features , 1999, IEEE Trans. Robotics Autom..

[19]  Yoshiki Kobayashi,et al.  Multitype lane markers recognition using local edge direction , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[20]  Bart De Schutter,et al.  IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief , 2005 .

[21]  Monson H. Hayes,et al.  A Novel Lane Detection System With Efficient Ground Truth Generation , 2012, IEEE Transactions on Intelligent Transportation Systems.

[22]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[23]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[24]  Paul A. Viola,et al.  Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.

[25]  M. Aizerman,et al.  Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .

[26]  Alexei A. Efros,et al.  An empirical study of context in object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[28]  Mohan M. Trivedi,et al.  Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation , 2006, IEEE Transactions on Intelligent Transportation Systems.

[29]  Miguel Torres-Torriti,et al.  A comparison of gradient versus color and texture analysis for lane detection and tracking , 2009, 2009 6th Latin American Robotics Symposium (LARS 2009).

[30]  Mohamed Aly,et al.  Real time detection of lane markers in urban streets , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[31]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[32]  R. Chellappa,et al.  Video-based Lane Detection using Boosting Principles , 2009 .

[33]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Saburou Saitoh,et al.  Theory of Reproducing Kernels and Its Applications , 1988 .

[35]  Gunnar Rätsch,et al.  Boosting Algorithms for Maximizing the Soft Margin , 2007, NIPS.

[36]  Alexander Zelinsky,et al.  Robust vision based lane tracking using multiple cues and particle filtering , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[37]  Christopher Rasmussen,et al.  Combining laser range, color, and texture cues for autonomous road following , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[38]  Stefano Ghidoni,et al.  TerraMax Vision at the Urban Challenge 2007 , 2010, IEEE Transactions on Intelligent Transportation Systems.

[39]  Antonio Torralba,et al.  Contextual Priming for Object Detection , 2003, International Journal of Computer Vision.

[40]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[41]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[42]  Ramakant Nevatia,et al.  Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors , 2007, International Journal of Computer Vision.

[43]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[44]  Sergiu Nedevschi,et al.  Probabilistic Lane Tracking in Difficult Road Scenarios Using Stereovision , 2009, IEEE Transactions on Intelligent Transportation Systems.

[45]  Hsu-Yung Cheng,et al.  Lane Detection With Moving Vehicles in the Traffic Scenes , 2006, IEEE Transactions on Intelligent Transportation Systems.

[46]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[47]  Massimo Bertozzi,et al.  GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection , 1998, IEEE Trans. Image Process..

[48]  Toshihiro Wakita,et al.  On the Use of Stochastic Driver Behavior Model in Lane Departure Warning , 2011, IEEE Transactions on Intelligent Transportation Systems.

[49]  MengChu Zhou,et al.  Optimal Lane Reservation in Transportation Network , 2012, IEEE Transactions on Intelligent Transportation Systems.

[50]  Yoav Freund,et al.  An Adaptive Version of the Boost by Majority Algorithm , 1999, COLT '99.

[51]  Rama Chellappa,et al.  Online Empirical Evaluation of Tracking Algorithms , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Dinggang Shen,et al.  Lane detection and tracking using B-Snake , 2004, Image Vis. Comput..