Robust Object Detection via Soft Cascade

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.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Lubomir D. Bourdev, Jonathan Brandt
• Keywords: Computer Vision, CVPR 2005, False Positive Rate, Roc Surface, Target Detection Rate, Training Object Detectors