Model-Free Detection, Encoding, Retrieval, and Visualization of Human Poses From Kinect Data

Abstract

The recognition of humans in Kinect camera data is a crucial problem in many mechatronics applications with human-computer interaction. In order to improve the limited scope of many methods based on a kinematic or surface mesh model, we propose a spatiotemporal segmentation of keypoints provided by a skeletonization of depth contours. A vector-shaped pose descriptor allows for the retrieval of similar poses and is easier to use with many machine learning libraries. A visualization method based on the Hilbert curve provides valuable insight in the detected poses. Our experimental results show that the proposed method is able to adapt to the number of people in a kitchen scenario, and track them over time. We were able to retrieve similar poses from a database and identify clusters in the dataset. By applying our method, the Princeton Tracking Benchmark, we demonstrated that our method is applicable in scenes where a human kinematic or surface mesh model would be overly restrictive.