Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection

A large variety of image features has been invented for detection of objects of a known class. We propose a framework to optimize the discrimination-efficiency tradeoff in integrating multiple, heterogeneous features for object detection. Cascade structured detectors are learned by boosting local feature based weak classifiers. Each weak classifier corresponds to a local image region, from which several different types of features are extracted. The weak classifier makes predictions by examining the features one by one; this classifier goes to the next feature only when the prediction from the already examined features is not confident enough. The order in which the features are evaluated is determined based on their computational cost normalized classification powers. We apply our approach to two object classes, pedestrians and cars. The experimental results show that our approach outperforms the state-of-the-art methods.