Toward accurate real-time marker labeling for live optical motion capture

Marker labeling plays an important role in optical motion capture pipeline especially in real-time applications; however, the accuracy of online marker labeling is still unclear. This paper presents a novel accurate real-time online marker labeling algorithm for simultaneously dealing with missing and ghost markers. We first introduce a soft graph matching model that automatically labels the markers by using Hungarian algorithm for finding the global optimal matching. The key idea is to formulate the problem in a combinatorial optimization framework. The objective function minimizes the matching cost, which simultaneously measures the difference of markers in the model and data graphs as well as their local geometrical structures consisting of edge constraints. To achieve high subsequent marker labeling accuracy, which may be influenced by limb occlusions or self-occlusions, we also propose an online high-quality full-body pose reconstruction process to estimate the positions of missing markers. We demonstrate the power of our approach by capturing a wide range of human movements and achieve the state-of-the-art accuracy by comparing against alternative methods and commercial system like VICON.