A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The image processing handbook (3rd ed.)
The image processing handbook (3rd ed.)
Locating objects using the Hausdorff distance
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Tyzx DeepSea High Speed Stereo Vision System
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 3 - Volume 03
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
Parts-based 3D object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Pedestrian detection using stereo and biometric information
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
IEEE Transactions on Intelligent Transportation Systems
A modified model for the Lobula Giant Movement Detector and its FPGA implementation
Computer Vision and Image Understanding
Research collaboration and ITS topic evolution: 10 years at T-ITS
IEEE Transactions on Intelligent Transportation Systems
Stereo-based object segmentation combining spatio-temporal information
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
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Pedestrians are the most vulnerable participants in urban traffic. The first step toward protecting pedestrians is to reliably detect them. We present a new approach for standing- and walking-pedestrian detection, in urban traffic conditions, using grayscale stereo cameras mounted on board a vehicle. Our system uses pattern matching and motion for pedestrian detection. Both 2-D image intensity information and 3-D dense stereo information are used for classification. The 3-D data are used for effective pedestrian hypothesis generation, scale and depth estimation, and 2-D model selection. The scaled models are matched against the selected hypothesis using high-performance matching, based on the Chamfer distance. Kalman filtering is used to track detected pedestrians. A subsequent validation, based on the motion field's variance and periodicity of tracked walking pedestrians, is used to eliminate false positives.