Improvements of Object Detection Using Boosted Histograms

We present a method for object detection that combines AdaBoost learning with local histogram features. On the side of learning we improve the performance by designing a weak learner for multi-valued features based on Weighted Fisher Linear Discriminant. Evaluation on the recent benchmark for object detection confirms the superior performance of our method compared to the state-of-the-art. In particular, using a single set of parameters our approach outperforms all methods reported in [5] for 7 out of 8 detection tasks and four object classes.