GPU and CPU Cooperative Accelerated Road Detection

In this paper, we propose a fast and robust unstructured road detection method that integrates GPU (Graphics Processing Unit) and CPU implementations. In order to ensure the robustness of the algorithm, BP (Back Propagation) Neural Network is employed to learn the color features from a set of sample of both road region and off-road region, and then to classify a newly pixel. And the B-spline curve model is employed to fit the boundaries of the lanes with the Least Square Method. To improve the real-time capability, the NVIDIA CUDA (Compute Unified Device Architecture) framework is used, and a GPU and CPU cooperative acceleration technique is proposed. Taking the advantages of these properties, the proposed implementation works out with high performance of detection in various environments. Meanwhile it is robust against noise, shadows and illumination variations. Moreover, it can performs about 10 times faster than a conventional implementation running on a CPU.

[1]  Keshou Wu,et al.  A fast and stable lane detection method based on B-spline curve , 2009, 2009 IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design.

[2]  Theo Gevers,et al.  3D Scene priors for road detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[4]  Cheng Xu,et al.  Non-parametic model for robust road recognition , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[5]  J. W. T. Youngs.  Curves and Surfaces , 1944 .

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

[7]  Antonio M. López,et al.  Road Detection Based on Illuminant Invariance , 2011, IEEE Transactions on Intelligent Transportation Systems.

[8]  M. Foedisch,et al.  Adaptive real-time road detection using neural networks , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[9]  Claire Mathieu,et al.  Multilayer Neural Networks: One or Two Hidden Layers? , 1996, NIPS.

[10]  Takashi Machida,et al.  GPU & CPU cooperative accelerated pedestrian and vehicle detection , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).