International Journal of Computer Vision
A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
A Flexible New Technique for Camera Calibration
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
Fast Compact City Modeling for Navigation Pre-Visualization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Pedestrian Detection with Stereo Vision
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D-3D-based on-board pedestrian detection system
Computer Vision and Image Understanding
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
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We present a novel approach for pedestrian detecting on moving vehicle which equipped with low-cost cameras. Our approach is working in a framework which combines two-dimensional human body characteristics and three-dimensional information such as parallax and distance. By constructing a SPM (surface parallax map), it calculates parallax of object which do not belong to the road plane such as human body and obstacles. After recording the scores of all road area, an occlusion image is created, in which high density area indicates people's most likely appearance. Then a SVM (support vector machine) classifier is trained to classify pedestrian and non-pedestrian windows in candidate area. We also propose an algorithm to maintain SPM in real time. We evaluate our approach on real data which are taken from crowded city areas, the efficient and accurate results are demonstrated.