Self-Organizing Cascaded Structure of Deformable Part Models for Fast Object Detection

In this paper, we propose a framework which self-organizes the cascaded object detection filters for fast object detection with maintaining high accuracy. The proposed scheme consists of root and part filter modules, which are cascaded in a self-organizing structure. The pruning of non-object regions in low resolution at the root cascade stage is critical for the object detection speed. At root stage, to prune as many non-object regions as possible, we build a root cascade structure using multiple root models. These models are obtained via bagging procedure for non-linear classification of object and non-object parts in an image. Additional speed-up is achieved by determining proper deployment order of part models. We define a discriminability measure for the part models and suggest a self-organizing scheme to generate an efficient order of part models. The proposed method is evaluated through computational experiments with the PASCAL VOC and INRIA datasets, as a result, our method achieves on average more than 2 times faster performance than the original cascade-DPM, with comparable precision scores.