We describe a method for training object detectors using a generalization of the cascade architecture, which results in a detection rate and speed comparable to that of the best published detectors while allowing for easier training and a detector with fewer features. In addition, the method allows for quickly calibrating the detector for a target detection rate, false positive rate or speed. One important advantage of our method is that it enables systematic exploration of the ROC surface, which characterizes the trade-off between accuracy and speed for a given classifier.
Robust object detection via soft cascade
2005-01-01
513065 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Background Learnable Cascade for Zero-Shot Object Detection
British Library Conference Proceedings | 2021
|Object Detection Using a Cascade of 3D Models
Springer Verlag | 2006
|Object Detection Using a Cascade of 3D Models
British Library Conference Proceedings | 2006
|VEHICLE DETECTION FOR AUTONOMOUS PARKING USING A SOFT-CASCADE ADABOOST CLASSIFIER
British Library Conference Proceedings | 2014
|