Verification and Regularization Method for 3D-Human Body Pose Estimation based on Prior Knowledge

Abstract

Applications used in human-centered scene analysis often rely on AI processes that provide the 3D data of human bodies. The applications are limited by the accuracy and reliability of the detection. In case of safety applications, an almost perfect detection rate is required. The presented approach gives a confidence measure for the likelihood that detected human bodies are real persons. It measures the consistency of the estimated 3D pose information of body joints with prior knowledge about the physiologically possible spatial sizes and proportions. Therefore, a detailed analysis was done which lead to the development of an error metric that allows the quantitative evaluation of single limbs and in summary of the complete body. For a given dataset an error threshold has been derived that verifies 97% persons correctly and can be used for the identification of false detections, so-called ghosts. Additionally, the 3D-data of single joints could be rated successfully. The results are usable for relabeling and retraining of underlying 2D and 3D pose estimators and provides a quantitative and comparable verification method, which improves significantly a reliable 3D-recognition of real persons and increases hereby the possibilities of high-standard applications of 3D human-centered technologies.