Online Learning Asymmetric Boosted Classifiers for Object Detection

We present an integrated framework for learning asymmetric boosted classifiers and online learning to address the problem of online learning asymmetric boosted classifiers, which is applicable to object detection problems. In particular, our method seeks to balance the skewness of the labels presented to the weak classifiers, allowing them to be trained more equally. In online learning, we introduce an extra constraint when propagating the weights of the data points from one weak classifier to another, allowing the algorithm to converge faster. In compared with the Online Boosting algorithm recently applied to object detection problems, we observed about 0-10% increase in accuracy, and about 5-30% gain in learning speed.