Robust human detection with super-pixel segmentation and random ferns classification using RGB-D camera

Abstract

Efficient and robust detection of humans has received great attention during the past few decades. This paper presents a two-staged approach for human detection in RGB-D images. As the traditional sliding window-based methods for target localization are often time-consuming, we propose to use the super-pixel method in depth data to efficiently locate the plausible head-top locations in the first stage. In the second stage, we propose to use Random Ferns to seek the features by combining information from different image spaces, which can select the most discriminative features and compute simple and fast Local Binary Features (LBFs) allowing for real-time applications. We evaluate our method on three publicly available challenging datasets taken by a Kinect camera. Experimental results demonstrate that the proposed approach can robustly detect humans in complicated environments.