Object Ranking on Deformable Part Models with Bagged LambdaMART

Abstract

Object detection methods based on sliding windows has long been considered a binary classification problem, but this formulation ignores order of examples. Deformable part models, which achieves great success in object detection, have the same problem.This paper aims to give better order to detections given by deformable part models. We use a bagged LambdaMART to model both pair-wise and list-wise relationships between detections. Experiments show our ranking models not only significantly improve detection rates compared to basic deformable part model detectors, but also outperform classification methods with same features.