3D Joints Estimation of the Human Body in Single-Frame Point Cloud

Abstract

Joint estimation of human body in point cloud is a key step for tracking human movements. In this work, we present a geometric method to achieve detection of the joints from a single-frame point cloud captured using a Time-of-Flight (ToF) camera. Three-dimensional (3D) human silhouette, as global feature of the single-frame point cloud, is extracted based on the pre-processed data, the angle and aspect ratio of the silhouette are subsequently utilized to perform pose recognition, and then 14 joints of human body are derived via geometric features of 3D silhouette. To verify this method, we test on an in-house captured 3D dataset containing 1200-frame depth images, which can be categorized into four different poses (upright, raising hands, parallel arms, and akimbo). Furthermore, we test on a subset of the G3D dataset. By hand-labelling the joints of each human body as the ground truth for validation and benchmarks, the average normalized error of our geometric method is less than 5.8 cm. When the distance threshold from the ground truth is 10 cm, the results demonstrate that our proposed method delivers improved performance with an average accuracy in the range of 90%.

Keywords

• Depth camera
• human pose detection
• joint detection
• sensor systems and applications