A novel distribution-based feature for rapid object detection

The discriminative power of a feature has an impact on the convergence rate in training and running speed in evaluating an object detector. In this paper, a novel distribution-based discriminative feature is proposed to distinguish objects of rigid object categories from background. It fully makes use of the advantage of local binary pattern (LBP) that specializes in encoding local structures and statistic information of distribution from training data, which is utilized in getting optimal separating hyperplane. The proposed feature maintains the merit of simplicity in calculation and powerful discriminative ability to distinguish objects from background patches. Three LBP-based features are derived to adaptive projection ones, which are more discriminative than original versions. The asymmetric Gentle Adaboost organized in nested cascade structure constructs the final detector. The proposed features are evaluated on two different object categories: frontal human faces and side-view cars. Experimental results demonstrate that the proposed features are more discriminative than traditional Haarlike features and multi-block LBP (MBLBP) features. Furthermore they are also robust in monotonous variations of illumination.