The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-road Path Following using Region Classification and Geometric Projection Constraints
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Reverse Optical Flow for Self-Supervised Adaptive Autonomous Robot Navigation
International Journal of Computer Vision
Model based vehicle detection and tracking for autonomous urban driving
Autonomous Robots
Learning Active Basis Model for Object Detection and Recognition
International Journal of Computer Vision
Vehicle Detection Using Partial Least Squares
IEEE Transactions on Pattern Analysis and Machine Intelligence
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
Image representation by active curves
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Automatically driving based on computer vision has attracted more and more attentions from both research and industrial fields. It has two main challenges, high road and vehicle detection accuracy and real-time performance. To study the two problems, we developed a driving simulation platform in a virtual scene. In this paper, as the first step of final solution, the Extreme Learning Machine (ELM) has been used to detect the virtual roads and vehicles. The Support Vector Machine (SVM) and Back Propagation (BP) network have been used as benchmark. Our experimental results show that the ELM has the fastest performance on road segmentation and vehicle detection with the similar accuracy compared with other techniques.