A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Boosting Chain Learning for Object Detection
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning-Based License Plate Detection Using Global and Local Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
Fast rotation invariant multi-view face detection based on real adaboost
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs
Journal of Visual Communication and Image Representation
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This paper proposes a frontal staircase detection algorithm using both classical Haar-like features and a novel set of PCA-base Haar-like features. Real AdaBoost is used for training a cascaded classifier. The PCA-based Haar-like features are extremely efficient at rejecting background regions at early stages in the cascade. A specifically designed scanning scheme made the algorithm constantly time efficient to different image sizes. An multi-detections integration scheme that is exclusive for staircase detection is extremely useful at further rejecting false positives. A new evaluation metric is proposed to rate each final detection, instead of Boolean classifying it. Experimental results show that the approach can detect staircases accurately at extremely low false positive rate.