Improving object localization using macrofeature layout selection

Woonhyun Nam, Bohyung Han, Joon Hee Han

Abstract: A macrofeature layout selection is proposed for object detection. Macrofeatures [2] are mid-level features that jointly encode a set of low-level features in a neighborhood. Our method employs line, triangle, and pyramid layouts, which are composed of several local blocks in a multi-scale feature pyramid. The method is integrated into boosting for detection, where the best layout is selected for a weak classifier at each iteration. The proposed algorithm is applied to pedestrian detection and compared with several state-of-the-art techniques in public datasets.