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Data Recording


Main PageMotive DocumentationData Recording

Page Scope

After a capture volume has been calibrated and all markers have been placed, you are now ready to capture Takes. In this page, we will cover concepts and tips that are important for the recording pipeline, such as setup steps for capture, recorded data types, and marker types.

For information on streaming of live tracking data, read through the Data Streaming page.

Live Mode and Edit Mode


There are two different modes in Motive: Live mode and Edit mode. You can toggle between two modes from the Control Deck or by using the (~) hotkey.

Live Mode

The Live mode is mainly used when recording new Takes or when streaming a live capture. In this mode, all of the cameras are continuously capturing 2D images and reconstructing the detected reflections into 3D data in real-time.
Motive in Live Mode.

Edit Mode

The Edit Mode is used for playback of captured Take files. In this mode, you can playback, or stream, recorded data. Also, captured Takes can be post-processed by fixing mislabeling errors or interpolating the occluded trajectories if needed.
Motive in Edit Mode

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Tip: Prime series cameras will illuminate in blue when in live mode, in green when recording, and turned-off in edit mode. See more at Prime Series Status Indicators

Recording


Recording in Motive is triggered from the Control Deck when in the Live mode, and the recorded data

Control Deck

In Motive, capture recording is controlled from the Control Deck. In the Live mode, new Take name can be assigned in the name box or you can just simply start the recording and let Motive automatically generate new names on the fly. You can also create empty Takes in the Data Management pane for a better organization. To start the capture, select Live mode and click the recording button (red). In the control deck, record time and frames are displayed in (Hour:Minute:Second:Frames).

Info2.png

Tip: For Skeleton tracking, always start and end the capture with a T-pose or A-pose, so that the skeleton assets can be redefined from the recorded data as well.

ControlDeckLabeled Live 111.png

Recorded Data Management

In Motive, all of the recorded capture files are managed through the Data Management pane. Each capture will be saved in a Take (TAK) file, which can be played back in the Edit mode later. Related Take files can be grouped within session folders. Simply create a new folder in the desired directory and load the folder onto the Data Management pane. Currently selected session folder is indicated with the flag symbol (SelectedFolder.png), and all newly recorded Takes will be saved in this folder.


DataManagementPane Overview.png


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Tip: Efficient ways of managing Takes


  1. Always start by creating session folders for organizing related Takes. (e.g. name of the tracked subject).
  2. Plan ahead and create a list of captures in a text file or a spreadsheet, and you can create empty takes by copying and pasting the list into the Data Management pane (e.g. walk, jog, run, jump).
  3. Once pasted, empty Takes with the corresponding names will be imported.
  4. Select one of the empty takes and start recording. The capture will be saved with the corresponding name.
  5. If the capture was unsuccessful, simply record the same Take again and another one will be recorded with a incremented suffix added at the end of the given Take name (e.g. walk_001, walk_002, walk_003). The suffix format is defined in the Application Settings.
  6. When captured successfully, select another empty Take in the list and capture the next one.


DataManagementPane ImportingFromText.png

Recorded Data Types

When a capture is first recorded, both 2D data and real-time reconstructed 3D data is saved onto the Take. For more details on each data type, refer to the Data Types page.

  • 2D data: The recorded Take file includes just the 2D object images from each camera.
  • 3D data: The recorded Take file also includes reconstructed 3D marker data in addition to 2D data.

Marker Types in Motive


Throughout capture, you might recognize that there are different types of markers that appear in the 3D perspective view. In order to correctly interpret the tracking data, it is important to understand the differences between these markers. There are three different displayed marker types: markers, rigid body markers, and bone (or skeleton) markers.

Markers

Marker data, labeled or unlabeled, is the reconstructed 3D positions of markers from the 2D images of each camera. These markers do not present rigid body or skeleton solver calculations but locate the actual marker position solely through the Point Cloud reconstruction engine. These markers are represented as a solid sphere in the viewport. By default, labeled markers are colored in white, and unlabeled markers are colored in orange as shown in the chart. Marker colors can be changed from the Application Settings.

DataRec Markers.png


Expected Marker Positions: Rigid Body Markers and Bone Markers

Rigid body markers or bone markers are expected marker positions. They appear as transparent spheres within a rigid body, or a skeleton, and they reflect the position that the rigid body or skeleton solver expects to find a corresponding reconstructed marker. Calculating these positions assumes that the marker is fixed on a rigid segment that doesn’t deform over the course of capture. When the rigid body solver or skeleton solver are correctly tracking reconstructed markers, both marker reconstructions, and expected marker positions will have similar position values and will closely align in the viewport.


DataRec Aligned.PNG DataRec CloseUnlabel.PNG DataRec unlabel.PNG
Figure 1. Left: Marker data aligned with the expected marker position Center: slight deviation from the expected position
Right: Marker is now unlabeled due to deviation from the expected position.


When creating rigid bodies, their associated markers will appear as a network of lines between markers. Skeleton marker expected positions would be located next to body segments, or bones. Please see Figure 2. If the marker placement is distorted during capture, the actual marker position will deviate from the expected position. Eventually, the marker may become unlabeled. Figure 1. shows how actual and expected marker positions could align or deviate from each other. Due to the nature of marker-based mocap systems, labeling errors may occur during capture. Thus, understanding each marker type in Motive is very important for correct interpretation of the data.

Read through the Labeling page for more information on marker labels.

DataRec SkeletalMarker.PNG DataRec RBMarkers.PNG
Figure 2. Left: skeletal bone markers Right: Rigid body markers.

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