Fourier Features For Person Detection in Depth Data

A robust and reliable person detection is crucial for many applications. In the domain of service robots that we focus on, knowing the location of a person is an essential requirement for any meaningful human-robot interaction. In this work we present a people detection algorithm exploiting RGB-D data from Kinect-like cameras. Two features are obtained from the data representing the geometrical properties of a person. These features are transformed into the frequency domain using Discrete Fourier Transform (DFT) and used to train a Support Vector Machine (SVM) for classification. Additionally, we present a hand detection algorithm based on the extracted silhouette of a person. We evaluate the proposed method on real world data from the Cornell Activity Dataset and on a dataset created in our laboratory.