Positioning and input is the key to VR technology, and leap MoTIon uses a recognition technology based on computer vision. Currently representative in the industry is Leap MoTIon's gesture recognition technology, of course, this is not Leap MoTIon's unique technology, the same technology is also used on hololens. Let's talk about it from the perspective of the API.
The structure of the Leap MoTIon sensor:
In general, the Leap sensor reconstructs the motion information of the palm in the real-world three-dimensional space based on the images captured by the two cameras from different angles. The detection range is generally between 25 mm and 600 mm above the sensor, and the space tested is generally an inverted quadrangular pyramid.
First, the Leap Motion sensor establishes a Cartesian coordinate system. The origin of the coordinates is the center of the sensor. The X axis of the coordinates is parallel to the sensor and points to the right of the screen. The y-axis points upwards. The Z axis points in the direction away from the screen. The unit is the real world of millimeters. Figure:
During use, the Leap Motion sensor periodically sends motion information about the hand, and each such message is called a "frame". Each such frame contains the detected:
a list of all palms and information;
a list of all fingers and information;
a list of hand tools (thin, straight, longer than a finger, such as a pen) and information;
All Pointable Objects, a list of all fingers and tools and information;
The Leap sensor assigns a unique identifier (ID) to all of them, which does not change when the palms, fingers, and tools remain within view. According to these IDs, you can query the information of each moving object through functions such as Frame::hand( ) and Frame::finger( ).
Leap can generate motion information based on the data detected in each frame and the previous frame. For example, if two hands are detected and both hands move in one direction, it is considered to be translation; if it is rotated like a ball, it is recorded as rotation. If both hands are close or apart, they are counted as zoom. The generated data contains:
Axial vector of rotation;
The angle of rotation (positive clockwise);
a matrix describing the rotation;
Scaling factor
Translation vector
For each hand, the following information can be detected:
The position of the center of the palm (three-dimensional vector, relative to the sensor coordinate origin, in millimeters);
The speed at which the palm moves (mm per second);
The normal vector of the palm (perpendicular to the palm plane, pointing from the palm of the hand);
The direction in which the palm is facing;
The center of the virtual sphere determined according to the curvature of the palm of the hand;
The radius of the virtual sphere determined according to the curvature of the palm of the hand;
Among them, the normal vector and direction of the palm are as shown below:
The center and radius of the "palm ball":
For each palm, information such as panning, rotation (such as turning the wrist to turn the palm), zooming (such as finger separation, aggregation) can also be detected. The detected data is the same as the global transformation, including:
Axial vector of rotation;
The angle of rotation (positive clockwise);
a matrix describing the rotation;
Scaling factor
Translation vector
In addition to detecting your fingers, Leap can also detect hand-held tools. As mentioned above, it is a thin, straight object that is longer than a finger:
For fingers and tools, they are collectively called Pointable Objects, and each Pointable Object contains this information:
length;
width;
direction;
Fingertip position;
Fingertip speed
The direction and fingertip position are as follows:
Based on global information, motion transformations, information and transformations of palms, fingers, and tools, developers can rely on these to create games and programs.
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