Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images

We present a two-step method to speed-up object detection systems in computer vision that use Support Vector Machines (SVMs) as classifiers. In a first step we perform feature reduction by choosing relevant image features according to a measure derived from statistical learning theory. In a second step we build a hierarchy of classifiers. On the bottom level, a simple and fast classifier analyzes the whole image and rejects large parts of the background. On the top level, a slower but more accurate classifier performs the final detection. Experiments with a face detection system show that combining feature reduction with hierarchical classification leads to a speed-up by a factor of 170 with similar classification performance.

• Type: Conference
• Year: 2001
• Where: CVPR
• Authors: Bernd Heisele, Thomas Serre, Sayan Mukherjee, Tomaso Poggio
• Keywords: Accurate Classifier Performs, Computer Vision, CVPR 2001, Fast Classifier, Feature Reduction, Object Detection Systems, Similar Classification Performance