Deep learning based target detection algorithm for motion capture applications

Abstract

Motion capture technology is the use of external devices to perform data recording and posture reproduction of the displacement of human structures. Deep learning algorithms are playing an increasingly important role in motion capture technology as the technology involves data that can be directly understood and processed by computers in terms of dimensional measurements, positioning of objects in physical space, and orientation determination. This paper presents an application of a convolutional neural network system, YOLO-V4, in the field of motion capture. YOLO-V4 system weight files are small and do not require high hardware requirements. It can also be implemented in PyTorch so that it can be deployed on mobile devices, enabling edge devices to run these models as well, relieving the space constraint of immovable signal capture devices and providing the advantages of high accuracy and high detection rate.