Abstract

Generic object detection is confronted by dealing with different degrees of variations in distinct object classes with tractable computations, which demands for descriptive and flexible object representations that are also efficient to eval-uate for many locations. In view of this, we propose to model an object class by a cascaded boosting classifier which integrates various types of features from competing local regions, named as regionlets. A regionlet is a base feature extraction region defined proportionally to a detec-tion window at an arbitrary resolution (i.e. size and as-pect ratio). These regionlets are organized in small groups with stable relative positions to delineate fine-grained spa-tial layouts inside objects. Their features are aggregated to a one-dimensional feature within one group so as to tol-erate deformations. Then we evaluate the object bound-ing box proposal in selective search from segmentation cues, limiting the evaluation locations to thousands. Our approach significantly outperforms the state-of-the-art on popular multi-class detection benchmark datasets with a single method, without any contexts. It achieves the detec-tion mean average precision of 41.7 % on the PASCAL VOC 2007 dataset and 39.7 % on the VOC 2010 for 20 object cat-egories. It achieves 14.7 % mean average precision on the ImageNet dataset for 200 object categories, outperforming the latest deformable part-based model (DPM) by 4.7%. 1.