Accurate and Efficient 3D Motion Tracking Using Deep Learning

A new sensing method has made tracking movement easier and more efficient. A research group from Tohoku University has captured dexterous 3D motion data from a flexible magnetic flux sensor array, using deep learning and a structure-aware temporal bilateral filter. "We can now track complex motions with higher accuracy," said Yoshifumi Kitamura, co-author of the study. Dexterous 3D motion data can be used for multiple purposes: biologists can use the data to record detailed movements of small animals in their living environments, scientists can track the flow of fluids, and researchers can track finger movements and objects being manipulated by users in virtual reality. Currently, optical cameras are the most prominent method of tracking movements.