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

摘要

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. criterion of the classification algorithm to select the optimal feature subset. Wrapper methods can provide more accurate solutions than filter methods, but in general are more computationally expensive. We present a new wrapper method to reduce the dimensions of both input and feature space of an SVM. An alternative approach for speeding-up SVM classification has been proposed in by reducing the number of support vectors. Feature reduction is a generic tool that can be applied to any classification problem. When dealing with a specific classification task we can use prior knowledge about the type of data to speed- up classification.