Gesture-controlled interfaces for self-service machines and other applications
A gesture recognition interface for use in controlling self-service machines and other devices is disclosed. A gesture is defined as motions and kinematic poses generated by humans, animals, or machines. Specific body features are tracked, and static and motion gestures are interpreted. Motion gestures are defined as a family of parametrically delimited oscillatory motions, modeled as a linear-in-parameters dynamic system with added geometric constraints to allow for real-time recognition using a small amount of memory and processing time. A linear least squares method is preferably used to determine the parameters which represent each gesture. Feature position measure is used in conjunction with a bank of predictor bins seeded with the gesture parameters, and the system determines which bin best fits the observed motion. Recognizing static pose gestures is preferably performed by localizing the body/object from the rest of the image, describing that object, and identifying that description. The disclosure details methods for gesture recognition, as well as the overall architecture for using gesture recognition to control of devices, including self-service machines.
1. A map control method, comprising the steps of:
storing edge information relating to a plurality of gestures;
imaging a gesture-making target;
using a mathematical operator to find the edges of the target;
comparing the edges of the target as imaged to the stored edge information to determine a control operation; and
applying the control operation to the generation, selection, presentation, or movement of a map.
2. The method of claim 1 , wherein the target is a human hand.
3. The method of claim 1 , further including the step of generating a bounding box around the target.
4. The method of claim 1 , wherein the recognized gestures are static or dynamic.
5. The method of claim 1 , wherein the map is a weather map.