Improved response modelling on weak classifiers for boosting

This paper demonstrates a method of increasing the quality of weak classifiers in the boosting context by using improved response modelling. The new method improves upon the results of a recent response binning approach proposed by Rasolzadeh et al. [1]. For experimental purposes the improved method is applied to the familiar Haar features as used by Viola and Jones in their face/pedestrian detection systems. However, the methods benefits are general and therefore not restricted to this particular feature type. Unlike many previous methods, this method is suitable for modelling multi-modal responses and is highly resistant to overfitting. It does this by adaptively choosing suitable support regions around the values taken by the standard response binning method. More accurate models are produced, with particular improvement around the final decision boundary. It is shown that the new method can be trained with one tenth of the training data required to achieve similar results on previous methods. This substantially lowers the overall training time of the system. The method's ability to consistently produce better hypotheses over a variety of pedestrian detection tasks is shown.