Properties: Take

Main PageProperties paneProperties: Take

When a Take is selected from the Data Management pane, related information will be displayed in the Properties pane.

From the Properties pane, you can get the general information about the Take, including a total number of recorded frames, capture data/time, and the list of assets


Take properties listed under the Properties pane.

Name: Take name.

Frame Rate: The camera frame rate in which the take was captured. The Take file will contain the corresponding number of frames for each second.

Start Frame: The frame ID of the first frame saved on the Take.

End Frame: The frame ID of the last frame saved on the Take.

Start Time: A timestamp of when the recording was first captured started.

End Time: A timestamp of when the recording was ended.

Assets: Names of assets that are included in the Take

Notes: Comments regarding the take can be noted here for additional information.

Best: Marks the best take. Takes that are marked as best can also be accessed via Motive Batch Processor scripts.

Capture Data/Time: Date and time when the capture was recorded.

Captured in Version: The version of Motive which the Take was recorded in. (This applies only to Takes that were captured in versions 1.10 or above)

Captured in Build: The build of Motive which the Take was recoreded in.

Health State: The data quality of the Take which can be flagged by users.

Progress: Progress indicator for showing how into the post-processing workflow that this Take has made.

Reconstruction Settings[edit]

Reconstruction settings under the Take properties are the Point Cloud Engine parameters that were used to obtain the 3D data saved in the Take file. In Edit mode, you can change these parameters and perform the post-processing reconstruction pipeline to obtain a new set of 3D data with the modified parameters.

Reconstruction Settings

Maximum Residual (mm)[edit]

Default: 10.00 mm

The residual value sets the maximum allowable offset distance (in mm) between rays contributing to a single 3D point.

When the residual value is set too high, unassociated marker rays may contribute to marker reconstruction, and non-existing ghost markers may be reconstructed. When this value is set too low, the contributing rays within a marker could reconstruct multiple markers where there should only be one.

Choosing a good Residual Value[edit]

Depending on the size of markers used, the contributing rays will converge with a varying tolerable offset. If you are working with smaller markers, set the residual value lower. If you're working with larger markers, set this value higher because the centroid rays will not converge as precisely as the smaller markers. A starting point is to set the residual value to the diameter of the smallest marker and go down from there until you start seeing ghost markers. For example, when 3 mm and 14 mm markers are captured in a same volume, set the residual value to less than 3 mm. The ghost markers can appear on larger markers if this value is set too low.

The residual can also be viewed as the minimum distance between two markers before they begin to merge. If two markers have a separation distance smaller than the defined residual (in mm), the contributing rays for each marker will be merged and only one marker will be reconstructed, which is undesirable. Remember that for a 3D point to be reconstructed, it needs to have at least two rays contributing to a marker depending on the Minimum Rays setting.

If calibration quality is not very good, you may need to set this value higher for increased tolerance. This will work only if your markers are further apart in the 2D views throughout the given marker motion. This is because there is more errors in the system. However, for best results, you should always work with a calibration with minimal error (See Calibration).

  • Multiple tracked rays converging into a reconstructed marker.
  • Average residual value for a selected marker.
  • Maximum Ray Length (m)[edit]

    Default: None — the calibration solver will set a suggested distance based on the wanding results, but this can still be adjusted by the user after calibration.

    This sets the maximum distance, in meters, a marker can be from the camera to be considered for 3D reconstruction. In very large volumes with high resolution cameras, this value can be increased for a longer tracking range or to allow contributions from more cameras in the setup. This setting can also be reduced to filter out longer rays from reconstruction. Longer rays generally produce less accurate data than shorter rays.

    When capturing in a large-size volume with a medium-size – 20 ~ 50 cameras – camera system, this setting can be adjusted for better tracking results. Tracking rays from cameras at the far end of the volume may be inaccurate for tracking markers on the opposite end of the volume, and the unstable rays may contribute to ghost marker reconstructions. In this case, lower the maximum ray length to restrict reconstruction contributions from cameras tracking at long distances. For captures vulnerable to frequent marker occlusions, adjusting this constraint is not recommended since more camera coverage is needed for preventing the occlusions. Note that lowering this setting can take a toll on performance at higher camera counts and marker counts because the solver has to perform numerous calculations per second to decide which rays are good.

    Minimum Ray Length (m)[edit]

    Default: 0.2 m

    This sets the minimum distance, in meters, between a marker and a camera for the camera to contribute to the reconstruction of the marker. When ghost markers appear close to the camera lens, increase this setting to restrict the unwanted reconstructions in the vicinity. But for close-range tracking applications, this setting must be set low.

    Minimum Ray Count[edit]

    Default: 2 rays

    This sets the required minimum number of cameras that must see a marker for it to be reconstructed.

    For a marker to be reconstructed, at least two or more cameras need to see the marker. The minimum rays setting defines the required number of cameras that must see a marker for it to be reconstructed. If you have 4 cameras and set this to 4, all cameras must see the marker; otherwise, the marker will not be reconstructed and the contributing rays will become the untracked rays.

    When more rays are contributing to a marker, more accurate reconstruction can be achieved, but generally, you don't need all cameras in a setup to see a marker. If you have a lot of cameras capturing a marker, you can safely increase this setting to prevent false reconstructions which may come from 2 or 3 rays that happen to connect within the residual range. However, be careful when increasing this setting because a high number of minimum rays requirement may decrease the effective capture volume and increase the frequency of marker occlusions during capture.

    Marker Labeling Mode[edit]

    Default: Passive

    Configures Motive for tracking either the passive markers, the synchronized active markers, or both. See Active Marker Tracking for more information.

    Active Patten Depth[edit]

    Default: 12

    This setting is available only if marker labeling mode is set to one of the active marker tracking modes. This setting sets the complexity of the active illumination patterns. When tracking a high number of rigid body, this may need to be increased to allow for more combinations of the illumination patterns on each marker. When this value is set too low, the active labeling will not work properly.

    Continuous Calibration[edit]

    Default: Disabled

    Enable or disable continuous calibration. When enabled, Motive will continuously monitor the calibration quality and update it as necessary. For more information, refer to the Continuous Calibration page.



    This property was called Ray Ranking in older versions

    Default: 4

    This setting enables the Ray Ranking, which calculates quality of each ray to potentially improve the reconstruction. Setting this to zero means that ray ranking is off, while 1 through 4 set the number of the evaluation iterations; 4 being 4 iterations. Setting this value to the max of 4 will slow down the reconstruction process but will produce more accurate results.

    The Ray Ranking increases the stability of the reconstruction but at a heavy performance cost. The ray quality is analyzed by comparing convergence of rays that are contributing to the same marker. An average converging point is calculated, and each ray is ranked starting from the one closest to the converging point. Then, each ray is weighed differently in the Point Cloud reconstruction engine according to the assigned rankings.

    This setting is useful especially when there are multiple rays contributing to a marker reconstruction. If you're working with small to medium marker counts, enabling this will not have an evident improvement on performance. Also, when precise real-time performance is required, disable this setting especially for a setup with numerous cameras.

    Pixel Gutter (pixels)[edit]

    Default: 0 pixels

    Establishes a dead zone, in pixels, around the edge of the 2D camera image. Any 2D objects detected within this gutter will be discarded before calculating through the point cloud. In essence, it is a way of getting only the best data of the captured images, because markers seen at the edges of the camera sensor tend to have higher errors.

    This setting can be increased in small amounts in order to accommodate for cases where lens distortions are potentially causing problem with tracking. Another use of the setting for limiting the amount of data going to the reconstruction solver, which may help when you have a lot of markers and/or cameras. Be careful adjusting this setting as the trimmed data can't be reacquired in post-processing pipelines.

    Minimum Angle (degrees)[edit]

    Default: 5 degrees

    The minimum allowable angle – in degrees from the marker's point of view – between the rays to consider them valid for marker reconstruction. This separation also represents the minimum distance required between the cameras. In general, cameras should be placed with enough distance in between in order to capture unique views on the target volume. For example, if there are only two cameras, an ideal reconstruction would occur when the cameras are separated far enough so the rays converge with a 90 degree of an incident angle from the perspective of the reconstructed marker(s).

    When working with a smaller-sized system with a fewer number of cameras, there will be only a limited number of markers rays that can be utilized for reconstruction. In this case, lower this setting to allow reconstruction contributions from even the cameras that are in close vicinity to each other.

    On the other hand, when working with a large system setup with a lot of cameras, you can set this value a bit higher to limit marker rays that are coming from the cameras that are too close together. Similar vantages obtained by the cameras within vicinity do not necessarily contribute unique positional data to the reconstruction, but they only increase the required amount of computation. Rays coming from very close cameras may increase the error in the reconstruction. Better reconstruction can only be achieved with a good, overall camera coverage (See Camera Placements).

    Rigid Body Markers Override[edit]

    Default: False

    When the Rigid Body Marker Override is set to True, Motive will replace observed 3D markers with the rigid body's solution for those markers. 3D tracking data of reconstructed and labeled trajectories will be replaced by the expected marker locations of the corresponding rigid body solve.

    This is applicable only for rigid bodies using Ray-Based tracking, and when the Use Smart Markers is enabled.

    Use Smart Markers[edit]

    Default: True

    When this feature is enabled, Motive uses expected marker locations from both the model solve and the trajectory history to create virtual markers. These virtual markers are not direct reconstructions from the Point Cloud engine. When the use of smart markers is enabled, rigid body and skeleton asset definitions will also be used in conjunction with 2D data and reconstructed 3D data to facilitate reconstruction of additional 3D marker locations to improve tracking stability. These virtual markers are created to make live data match recorded data in situations where model and history data helped to improve the live solve

    More specifically, for rigid body tracking, Motive will utilize untracked rays along with the rigid body asset definition to replace the missing markers in the 3D data. In order to compute these reconstructions, the rigid body must be using the Ray-Based tracking algorithm. For skeleton tracking, only the asset definitions are used to approximate virtual reconstruction at the location where the occluded marker was originally expected according to the corresponding skeleton asset.

    Using the asset definitions in obtaining the 3D data could be especially beneficial for accomplishing stable tracking of the assets in low camera count systems where all of the reconstructions may not always meet the minimum required tracked ray requirements.


    Usage note. In 2.0, trajectories of virtually created markers on a skeleton segment may not get plotted on the graph view pane.

    Utilize Active Labels[edit]

    Default: true

    When set to true, Motive will recognize the unique illuminations from synchronized active markers and perform active labeling on its reconstructions. If you are utilizing our active marker solution, this must be set to true. For more information about active labeling, read through the Active Marker Tracking page.

    Max Gap Span[edit]

    Default: 20

    Sets the required minimum number of frames without occlusion for a tracked marker to be recognized as the same reconstruction to form a trajectory. If a marker is hidden, or occluded, longer than the defined number of frames, then the trajectory will be truncated and the marker will become unlabeled.

    Max Search Radius (m)[edit]

    Default: 0.06 m

    To identify and label a marker from one frame to the next, a prediction radius must be set. If a marker location in the subsequent frame falls outside of the defined prediction radius, the marker will no longer be identified and become unlabeled. This is not the case when the marker is part of a tracked rigid body or skeleton. The prediction radius has no direct influence on reconstruction and is used only for marker labeling.

    For capturing relatively slow motions with tight marker clusters, limiting the prediction radius will help maintaining precise marker labels throughout the trajectory. Faster motions will have a bigger frame to frame displacement value and the prediction radius should be increased. When capturing in a low frame rate settings, set this value higher since there will be bigger displacements between frames.

    Reconstruction Bounds[edit]

    Reconstruction bounds applied.

    Bound Reconstrutction[edit]

    (Default: False) When set to true, the 3D points will be reconstructed only within the given boundaries of the capture volume. The minimum and maximum boundaries of X/Y/Z axis are defined in the below properties.

    Visible Bounds[edit]

    Visualize the reconstruction bounds in the 3D viewport.

    Bounds Shape[edit]

    (Default: Cuboid) This setting selects the shape of the reconstruction bound. You can select from cuboid, cylinder, spherical, or ellipsoid shapes and the corresponding size and location parameters (e.g. center x/y/z and width x/y/z) will appear so that the bound can be customized to restrict the reconstruction to a certain area of the capture volume.


    Auto-labeler section in the Reconstruction pane.

    After markers have been reconstructed in Motive, they must be labeled. Individual markers can be manually labeled, but the auto-labeler simplifies this process using the Assets. Rigid body and skeleton assets, created in Motive, saves their marker arrangement definitions and uses them to auto-label corresponding marker sets within the Take. The auto-labeling, is a process of associating 3D marker reconstructions in multiple captured frames by assigning marker labels within the defined constraints. After the labeling process, each of the labeled markers provides respective 3D trajectories throughout the Take.

    Pose Detection[edit]

    Default: True
    Pose detection improves the stability of skeleton tracking by detecting standing poses. For multi-skeleton captures, this feature may increase the skeleton solve latency.

    Minimum Key Frames[edit]

    Default: 2
    This setting sets the required minimum number of frames for each trajectory in the recorded 3D data. Any trajectories with a length less than the required minimum will be discarded from the 3D data after running the auto-labeling pipeline.

    Auto-labeler Passes[edit]

    Default: 1
    The number of iterations for analyzing detected marker trajectories for maintaining constant marker labels. Increasing this setting can improve the marker auto-labeling, but more iterations will require more time and computation effort to complete the auto-labeling.

    Rigid Body Assisted Labeling[edit]

    Rigid Body Assisted Labeling of markers on the hand results in less broken trajectories
    Rigid Body Assisted Labeling can be used to optimize the labeling of markers within a region defined by a rigid body. The first step in using this feature is to create a rigid body from markers that are visible and rigidly connected. The example shown in the figure below demonstrates this for hand tracking. Five white markers are selected on the top of the wrist - which is rigidly defined. The black markers on the fingers are not rigidly defined in any fashion but are within the boundary of the Rigid Body Assisted Labeler. Labeling continuity is improved for the markers on the fingers which are given automatic labels.
    Tracking of organic or flexible objects - that do not have a tracking models like the face and hand, are good candidates for Rigid Body Assisted Labeling.

    Rigid Body-Assisted Labeling[edit]

    Default: False
    Enable or disable rigid body assisted labeling feature.

    Rigid Body Volume Radius[edit]

    Default: 300 mm
    The rigid body volume radius defines the region of space where the rigid body assisted labeling is applied. Increasing this radius will increase time needed for the auto-labeling so care should be made when setting this property.

    Prediction Radius (mm)[edit]

    Default: 10 mm
    The prediction radius defines the size of the bounding region used to label markers. When labeling a marker from one frame to the next, a bounding region, relative to the rigid body, is created around each labeled marker. The labeling continuity is restricted to the bounding region from frame to frame. Increasing this can allow markers to swap if there are occlusions in the data. Decreasing this restricts labeling from frame to frame but may lead to an increase in broken trajectories.

    Maximum Assisted Labeling Gap[edit]

    Default: 30 frames
    The maximum gap frames property defines the maximum number of frames a marker can be hidden before it is truncated or unlabeled. Increase this value if larger gaps are to be anticipated. Increasing the assisted labeling gap will increase the processing time of reconstruction.

    Discard External Markers[edit]

    Default: False
    Discards markers outside of rigid body volume. Enabling this property will eliminate marker reconstructions outside of the region defined by the Rigid Body Volume.

    Dynamic Constraints[edit]

    Default: None
    Prevents the rigid body from moving/rotation more than specified amount per frame.
    Max Translation (mm)[edit]
    Default: 100
    Distance for Dynamic Translation Constraint option.
    Max Rotation (deg)[edit]
    Default: 30
    Angle for the Dynamic Rotation Constraint option.
    Minimum Tracking Frames[edit]
    Default: 20
    Dynamic constraints are enabled when the rigid body is consecutively tracking more than this frame count.

    Marker Filter Diameters[edit]

    Default: False
    Markers less than this diameter will not be used for rigid body tracking.
    Minimum Diameter (mm)[edit]
    Default: 10
    Diameter used for Marker Filter Diameter option.

    Calibration Info[edit]

    Camera system calibration details for the selected Take. Takes recorded in older versions of Motive may not contain this data.

    Calibration Time Stamp: Shows when the cameras were calibrated.

    Residual Mean Error: Shows mean residual offset value during calibration.

    Reisdual 50/95/99 Percentile Error: Displays percentile distribution of the residual errors.

    Wand Mean Error: Displays a mean error value of the detected wand length samples throughout the wanding process.

    Wand 50/95/99: Displays percentile distribution of the wand errors.

    Calibration Wand: Shows what type of wand was used: Standard, Active, or Micron series.

    Wand Length: Displays the length of the calibration wand used for the capture.

    Wand Center Offset: Distance from one of the end markers to the center marker, specifically the shorter segment.