Real-time full-body human attribute classification in RGB-D using a tessellation boosting approach

Abstract

Robots that cooperate and interact with humans require the capacity to detect and track people, analyze their behavior and understand human social relations and rules. A key piece of information for such tasks are human attributes like gender, age, hair or clothing. In this paper, we address the problem of recognizing such attributes in RGB-D data from varying full-body views. To this end, we extend a recent tessellation boosting approach which learns the best selection, location and scale of a set of simple RGB-D features. The approach outperforms the original approach and a HOG baseline for five human attributes including gender, has long hair, has long trousers, has long sleeves and has jacket. Experiments on a multi-perspective RGB-D dataset with full-body views of over a hundred different persons show that the method is able to robustly recognize multiple attributes across different view directions and distances to the sensor with accuracies up to 90%. Our methods runs in real-time, achieving a classification rate of around 300 Hz for a single attribute.