Structured Hough Voting for Vision-Based Highway Border Detection

We propose a vision-based highway border detection algorithm using structured Hough voting. Our approach takes advantage of the geometric relationship between highway road borders and highway lane markings. It uses a strategy where a number of trained road border and lane marking detectors are triggered, followed by Hough voting to generate corresponding detection of the border and lane marking. Since the initially triggered detectors usually result in large number of positives, conventional frame-wise Hough voting is not able to always generate robust border and lane marking results. Therefore, we formulate this problem as a joint detection-and-tracking problem under the structured Hough voting model, where tracking refers to exploiting inter-frame structural information to stabilize the detection results. Both qualitative and quantitative evaluations show the superiority of the proposed structured Hough voting model over a number of baseline methods.

[1]  Silvio Savarese,et al.  Multiple Target Tracking in World Coordinate with Single, Minimally Calibrated Camera , 2010, ECCV.

[2]  Antonio Criminisi,et al.  TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context , 2007, International Journal of Computer Vision.

[3]  BebisGeorge,et al.  On-Road Vehicle Detection , 2006 .

[4]  Jean Ponce,et al.  General Road Detection From a Single Image , 2010, IEEE Transactions on Image Processing.

[5]  Stevica Graovac,et al.  Detection of Road Image Borders Based on Texture Classification , 2012 .

[6]  Nick Barnes,et al.  Data-driven road detection , 2014, IEEE Winter Conference on Applications of Computer Vision.

[7]  Minkwang Lee,et al.  Road boundary detection and tracking for structured and unstructured roads using a 2D lidar sensor , 2014 .

[8]  Raúl Rojas,et al.  Camera based detection and classification of soft shoulders, curbs and guardrails , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[9]  Paul E. Rybski,et al.  Vision-based 3D bicycle tracking using deformable part model and Interacting Multiple Model filter , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  M. B. Wilson,et al.  Poppet: A Robust Road Boundary Detection and Tracking Algorithm , 1999, BMVC.

[11]  Martial Hebert,et al.  Co-inference for Multi-modal Scene Analysis , 2012, ECCV.

[12]  Urbano Nunes,et al.  Trainable classifier-fusion schemes: An application to pedestrian detection , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[13]  Nick Barnes,et al.  Learning Structured Hough Voting for Joint Object Detection and Occlusion Reasoning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Pierre Charbonnier,et al.  Road boundaries detection using color saturation , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[15]  Rama Chellappa,et al.  A Learning Approach Towards Detection and Tracking of Lane Markings , 2012, IEEE Transactions on Intelligent Transportation Systems.

[16]  Martial Hebert,et al.  Stacked Hierarchical Labeling , 2010, ECCV.

[17]  Jean Ponce,et al.  Vanishing point detection for road detection , 2009, CVPR.

[18]  B. V. K. Vijaya Kumar,et al.  Robust rear-view ground surface detection with hidden state conditional random field and confidence propagation , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[19]  Massimo Bertozzi,et al.  Stereo vision-based vehicle detection , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).