Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
The Geometry of Multiple Images: The Laws That Govern The Formation of Images of A Scene and Some of Their Applications
Journal of Global Optimization
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Stereo Vision-based approaches for Pedestrian Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Adaptative road lanes detection and classification
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Camera motion estimation by image feature analysis
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Real-time disparity contrast combination for onboard estimation of the visibility distance
IEEE Transactions on Intelligent Transportation Systems
An Efficient Approach to Onboard Stereo Vision System Pose Estimation
IEEE Transactions on Intelligent Transportation Systems
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
Reconstruction of non-rigid 3D shapes from stereo-motion
Pattern Recognition Letters
On-Board monocular vision system pose estimation through a dense optical flow
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
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This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head's position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs' brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach.