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

Timm Linder, Kai O. Arras
2015 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
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
more » ... 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.
doi:10.1109/iros.2015.7353541 dblp:conf/iros/LinderA15 fatcat:di6fyyzlorcmzbefdkuznwx7c4