Detecting Human in Still Images by Learning Multi-Scale Mid-Level Features

Detecting human in still images is one of the most challenging object detection problems. In this paper we apply the scale theory to human detection. By integrating Gaussian Pyramids multi-scale object representation approach we present a Learned Multi-scale Mid-level Feature (LMMF) based human detection algorithm. Firstly multiscale low-level features are extracted by Gaussian Pyramid decomposition and gradient computation. Then LMMFs are learned from multi-scale low-level features using AdaBoost algorithm. The final human/non-human decision is made by classification on the LMMFs. Using LMMF descriptors, our method attempts to harvest more information than using uni-scale feature descriptors. Experiments on INRIA person dataset demonstrate that our method outperforms the previous state of the art detector.