Gesture-controlled interfaces for self-service machines and other applications

• US 20060013440A1
• Filed: 09/14/2005
• Published: 01/19/2006
• Est. Priority Date: 08/10/1998
• Status: Active Grant

• Current Assignee: Jolly Seven, Series 70 of Allied Security Trust I (Allied Security Trust)
• Original Assignee: Brook Cavell, Charles J. Cohen, Charles J. Jacobus, Gene Foulk, George Paul, Glenn Beach, Jay Obermark
• Inventors: Cohen, Charles J., Jacobus, Charles J., Obermark, Jay, Cavell, Brook, Foulk, Gene, Paul, George, Beach, Glenn
• Granted Patent: US 7,460,690 B2
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: A63F 2300/1093 using visible light
• CPC Class Codes: A63F 2300/6045 for mapping control signals received from the input arrangement into game commands
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06V 40/28 Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113

Abstract

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.

Claims

1. A map control method, comprising the steps of:

storing information relating to a plurality of gestures;

imaging a gesture-making target;

comparing the target as imaged to the stored 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, further including the step of using an operator to find the edges of the target.

5. The method of claim 1, wherein the recognized gestures are static or dynamic.

6. The method of claim 1, wherein the map is a weather map.

7-8. -8. (canceled)