Machine Vision and Applications
A Rational Function Lens Distortion Model for General Cameras
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection
IEEE Transactions on Image Processing
TerraMax vision at the urban challenge 2007
IEEE Transactions on Intelligent Transportation Systems
Advanced safety sensor for gate automation
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Monocular rear-view obstacle detection using residual flow
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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This paper presents a robust method for close-range obstacle detection with arbitrarily aligned stereo cameras. System calibration is performed by means of a dense grid to remove perspective and lens distortion after a direct mapping between image pixels and world points. Obstacle detection is based on the differences between left and right images after transformation phase and with a polar histogram, it is possible to detect vertical structures and to reject noise and small objects. Found objects' world coordinates are transmitted via CAN bus; the driver can also be warned through an audio interface. The proposed algorithm can be useful in different automotive applications, requiring real-time segmentation without any assumption on background. Experimental results proved the system to be robust in several envitonmental conditions. In particular, the system has been tested to investigate presence of obstacles in blind spot areas around heavy goods vehicles (HGVs) and has been mounted on three different prototypes at different heights.