![]() ![]() A swap or noise can affect the movement of markers, and therefore affect the authenticity of the performance when it is later retargeted onto a CGI character. This will allow a solve of a skeleton, based on the positions and movements of the makers, to be created.Ī solve is reliant on the marker's accuracy. The end result of a 'tracked' file will be accurately labelled trajectories with minimum noise and no gaps. Swaps in data are often caused by the use of an automated labelling tool, or when the markers come into close proximity of each other, such as inner knees touching or hands touching together, resulting in the sofware misidentifying them. When there is enough data to produce a marker, but not enough for a reliable reconstruction, this will result in data with sporadic keyframes. ![]() In the reconstruction process, when there isn't enough data, the software will produce a gap where no data exists. However when an actor moves, does a particular stunt, or interacts with other actors, the maker's visibility can be lost to some or all cameras, preventing the software from getting enough information to allow the creation of the 3D marker. This means that the more cameras with vision, the better quality the data you'll capture. Each camera tracks a 2D view, and is set up and aimed to a position where a marker is being tracked by a minimum of three cameras at any time, which enables the reconstruction of 3D data. A motion capture system has a 360 degree view of a markered performer. Inaccuracies in data are often caused by occlusion of the physical marker. However most data gaps, jitters and other inaccuracies need to be manually' 'cleaned' or fixed before the data is ready to be exported to the next part of the pipeline. Within the software used, certain scripts or tools can be applied to automate the workflow. The role of data tracker is to ensure these post-process tasks are carried out correctly. These labels define what role each marker plays in the skeleton of the human body, translating the captured movements onto a primitive rig. The 'Labelling' process involves labelling these trajectories using a generic predefined character setup, to give each marker an identity associated with the underlying skeleton. However, there will be no identification as to what each piece of data is. ![]() However, we show such differences have little practical effect on reconstructed 3D kinematics.Optical data captured from a shoot is stored as multiple raw 2D data and requires a 'reconstruction' process combined with a calibration of the system taken on the day of shooting, to convert it into continuous 3D trajectories.Īt this stage the scene will contain a cloud of 3D data. ![]() As expected, the MOCAP obtained better 3D precision and accuracy. The reconstructed knee flexion angles were highly correlated (r 2>0.99) and showed no significant differences between systems (0.05). The ASC system provided a maximum error of 2.47 mm, about 10 times higher than the MOCAP (0.21 mm). To examine the accuracy of the ASC in respect to the knee flexion angles, a jump and gait task were also examined using one subject (Wilcoxon rank sum). The 3D precision was evaluated by the differences in the reconstructed position using a Bland-Altman test, while accuracy was assessed by a rigid bar test (Wilcoxon rank sum). Both systems were calibrated using the MOCAP protocol and the 3D markers coordinates of a T-shaped tool were reconstructed, concurrently. The aim of this study was to assess the precision and accuracy of an Action Sport Camera (ASC) system (4 GoPro Hero3+ Black) by comparison with a commercial motion capture (MOCAP) system (4 ViconMX40). ![]()
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