MPL-Boosted Integrable Features Pool for pedestrian detection

This paper presents a fast and accurate pedestrian detection method. To find a balance between speed and accuracy, we propose a Multi-Pose Learning Boosted Integrable Features Pool (MPL-Boosted IFP) approach. Our method achieves high recall-rate while taking the speed-advantage of cascade-of-rejectors approach. We build different types of feature sets, in which features are extremely fast to compute by using integral image. These features are used for building a large number of candidate weak classifiers by using linear SVM. Finally, MPL-Boost method selects the best weak classifiers suited for detection and construct the rejector-based cascade detector. The experiment results show our method achieve better detection precision than HOG and HOG-LBP classifier, meanwhile, speed up these methods near 30 times.