Evaluating the Accuracy of Cloud-based 3D Human Pose Estimation Tools: A Case Study of MOTiO by RADiCAL

The use of 3D Human Pose Estimation (HPE) has become increasingly popular in the field of computer vision due to its various applications in human-computer interaction, animation, surveillance, virtual reality, video interpretation, and gesture recognition. However, traditional sensor-based motion capture systems are limited by their high cost and the need for multiple cameras and physical markers. To address these limitations, cloud-based HPE tools, such as DeepMotion and MOTiON by RADiCAL, have been developed. This study presents the first scientific evaluation of MOTiON by RADiCAL, a cloud-based 3D HPE tool based on deep learning and cloud computing. The evaluation was conducted using the CMU dataset, which was filtered and cleaned for this purpose. The results were compared to the ground truth using two metrics, the Mean per Joint Error (MPJPE) and the Percentage of Correct Keypoints (PCK). The results showed an accuracy of 98 mm MPJPE and 96% PCK for most scenarios and genders. This study suggests that cloud-based HPE tools such as MOTiON by RADiCAL can be a suitable alternative to traditional sensor-based motion capture systems for simple scenarios with slow movements and little occlusion.