Non-flat Road Detection Based on A Local Descriptor

The detection of road surface and free space remains challenging for non-flat plane, especially with the varying latitudinal and longitudinal slope or in the case of multi-ground plane. In this paper, we propose a framework of the road surface detection with stereo vision. The main contribution of this paper is a newly proposed descriptor which is implemented in disparity image to obtain a disparity feature image. The road regions can be distinguished from their surroundings effectively in the disparity feature image. Because the descriptor is implemented in the local area of the image, it can address well the problem of non-flat plane. And we also present a complete framework to detect the road surface regions base on the disparity feature image with a convolutional neural network architecture.

[1]  N. Hautiere,et al.  Road Segmentation Supervised by an Extended V-Disparity Algorithm for Autonomous Navigation , 2007, 2007 IEEE Intelligent Vehicles Symposium.

[2]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Vincent Frémont,et al.  Exploiting fully convolutional neural networks for fast road detection , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Ethan Fetaya,et al.  StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation , 2015, BMVC.

[5]  Ho Gi Jung,et al.  Dense stereo-based critical area detection for active pedestrian protection system , 2012 .

[6]  Dai Yiruo,et al.  Complex ground plane detection based on V-disparity map in off-road environment , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[7]  Zhang Zhao-yang.  Depth Extraction Method Based on Multi-view Stereo Matching , 2010 .

[8]  Keiichi Uchimura,et al.  A complete U-V-disparity study for stereovision based 3D driving environment analysis , 2005, Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05).

[9]  Rudolf Mester,et al.  Free Space Computation Using Stochastic Occupancy Grids and Dynamic Programming , 2008 .

[10]  John G. Rarity,et al.  Obstacle detection using U-disparity on quadratic road surfaces , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[11]  Sergiu Nedevschi,et al.  Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection , 2010, IEEE Transactions on Vehicular Technology.

[12]  Jean-Philippe Tarel,et al.  Real time obstacle detection in stereovision on non flat road geometry through "v-disparity" representation , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[13]  Didier Aubert,et al.  A single framework for vehicle roll, pitch, yaw estimation and obstacles detection by stereovision , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[14]  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).

[15]  C. Teoh,et al.  Ground plane detection for autonomous vehicle in rainforest terrain , 2010, 2010 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology.

[16]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[17]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Le Thanh Sach,et al.  A robust road profile estimation method for low texture stereo images , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[19]  Uwe Franke,et al.  The Stixel World - A Compact Medium Level Representation of the 3D-World , 2009, DAGM-Symposium.

[20]  Fadi Dornaika,et al.  An Efficient Approach to Onboard Stereo Vision System Pose Estimation , 2008, IEEE Transactions on Intelligent Transportation Systems.

[21]  Yann LeCun,et al.  Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches , 2015, J. Mach. Learn. Res..

[22]  Markus Enzweiler,et al.  Efficient Stixel-based object recognition , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[23]  Naim Dahnoun,et al.  Stereo vision-based road estimation assisted by efficient planar patch calculation , 2016, Signal Image Video Process..

[24]  Timo Milbich,et al.  May I enter the roundabout? A time-to-contact computation based on stereo-vision , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[25]  Sergiu Nedevschi,et al.  Stereovision Approach for Obstacle Detection on Non-Planar Roads , 2004, ICINCO.

[26]  Daniel Cremers,et al.  B-Spline Modeling of Road Surfaces With an Application to Free-Space Estimation , 2009, IEEE Transactions on Intelligent Transportation Systems.