Dynamically Directing Interpretation of Input Data Based on Contextual Information

• US 20150378440A1
• Filed: 06/27/2014
• Published: 12/31/2015
• Est. Priority Date: 06/27/2014
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: Umlauf, Eike Jens, Preston, Andrew John, Marlow, Christopher Richard
• Application Number:
  US14/318,275
• Publication Number:
  US 20150378440A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 3/005 Input arrangements through a video camera
• 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: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training

Abstract

Technologies are described herein for dynamically directing an interpretation of input data based on contextual information associated with a virtual environment. According to one aspect of the disclosure, a computing device and a camera operate in concert to capture and interpret gestures of a human target to control a virtual skeleton, which may be visually represented as an avatar. Embodiments disclosed herein utilize filtering parameters in the interpretation of input data representing a state of the human target to generate output data that is used to direct the virtual skeleton and/or the avatar. The filtering parameters may be dynamically adjusted during runtime based on contextual information and other factors to dynamically change the way input data is interpreted. Dynamic adjustment of the filtering parameters during runtime may allow for an interpretation of input data that is more accurately aligned with a scenario presented in the virtual environment.

Claims

1. A computer-implemented method for processing a plurality of input data samples, wherein individual data samples of the plurality of input data samples indicate a state of an input object, and wherein the input object is represented by a virtual object, the computer-implemented method comprising:

associating, at a computing device, one or more weight values with the plurality of input data samples;

selecting, at the computing device, a threshold based on a scenario associated with the virtual object;

determining, at the computing device, if a product of the one or more weight values associated with the plurality of input data samples meets the threshold; and

changing a state of the virtual object, at the computing device, if it is determined that the product of the one or more weight values associated with the plurality of input data samples meets the threshold.

2. The computer-implemented method of claim 1, further comprising, selecting the one or more weight values, and wherein the one or more weight values are biased based on the scenario.

3. The computer-implemented method of claim 1, wherein the individual data samples of the plurality of input data samples describe an open state, a closed state or an unknown state, and wherein associating the one or more weight values with the plurality of input data samples comprises:

associating a low weight value to individual data samples describing the open state;

associating a middle weight value to individual data samples describing the unknown state;

associating a high weight value to individual data samples describing the closed state; and

determining the product of the one or more weight values by averaging the one or more weight values associated with the plurality of input data samples.

4. The computer-implemented method of claim 1, wherein the selection of the threshold is based on the scenario, the scenario defining a movement of the virtual object in a predetermined direction.

5. The computer-implemented method of claim 1, wherein the selection of the threshold is based on the scenario, the scenario defining a movement of the virtual object within a predetermined velocity range.

6. The computer-implemented method of claim 1, wherein the selection of the threshold is based on the scenario, the scenario defining a predetermined location of the virtual object.

7. The computer-implemented method of claim 1, wherein the selection of the threshold is based on the scenario, the scenario defining a location of the virtual object relative to a location of another object.

8. A computer storage medium having computer-executable instructions stored thereupon which, when executed by a computing device, cause the computing device to:

select one or more weight values, wherein the one or more weight values are biased based on a scenario;

associate the one or more weight values with a plurality of input data samples, wherein individual data samples of the plurality of input data samples indicate a state of an input object, and wherein the input object is represented by a virtual object;

determine if a product of the one or more weight values associated with the plurality of input data samples meets a threshold; and

change a state of the virtual object if it is determined that the product meets the threshold.

9. The computer storage medium of claim 8, wherein the computer-executable instructions further cause the computing device to select the threshold based on a scenario associated with the virtual object.

10. The computer storage medium of claim 8, wherein the individual data samples of the plurality of input data samples describe an open state, a closed state or an unknown state, and wherein associate the one or more weighting values to the plurality of input data samples comprises:

associating a low weight value to individual data samples describing the open state;

associating a middle weight value to individual data samples describing the unknown state;

associating a high weight value to individual data samples describing the closed state; and

determining the product of the one or more weight values by averaging the one or more weight values associated with the plurality of input data samples.

11. The computer storage medium of claim 8, wherein the selection of the one or more weight values is based on the scenario, the scenario defining a movement of the virtual object in a predetermined direction.

12. The computer storage medium of claim 8, wherein the selection of the one or more weight values is based on the scenario, the scenario defining a movement of the virtual object within a predetermined velocity range.

13. The computer storage medium of claim 8, wherein the selection of the one or more weight values is based on the scenario, the scenario defining a predetermined location of the virtual object.

14. The computer storage medium of claim 8, wherein the selection of the one or more weight values is based on the scenario, the scenario defining a location the virtual object relative to a location of another object.

15. A computing device, comprising:

a processor; and

a memory having computer-executable instructions stored thereupon which, when executed by the processor, cause the computing device to

select one or more weight values, wherein a biasing of the one or more weight values is based on a scenario,

associate one or more weight values with a plurality of input data samples, wherein individual data samples of the plurality of input data samples indicate a state of an input object, and wherein the input object is represented by a virtual object,

select a threshold based on the scenario associated with the virtual object,

determine if a product of the one or more weight values associated with the plurality of input data samples meets the threshold, and

changing a state of the virtual object if it is determined that the product of the one or more weight values associated with the plurality of input data samples meets the threshold.

16. The computing device of claim 15, wherein the individual data samples of the plurality of input data samples describe an open state, a closed state or an unknown state, and wherein associating the one or more weighting values with the plurality of input data samples comprises:

associating a low weight value to individual data samples describing the open state;

associating a middle weight value to individual data samples describing the unknown state;

associating a high weight value to individual data samples describing the closed state; and

determining the product of the one or more weight values by averaging the one or more weight values associated with the plurality of input data samples.

17. The computing device of claim 15, wherein the selection of the weight values and the threshold are based on the scenario, the scenario defining a movement of the virtual object in a predetermined direction.

18. The computing device of claim 15, wherein the selection of the weight values and threshold are based on the scenario, the scenario defining a movement of the virtual object within a predetermined velocity range.

19. The computing device of claim 15, wherein the selection of the weight values and threshold are based on the scenario, the scenario defining a predetermined location of the virtual object.

20. The computing device of claim 15, wherein the selection of the weight values and threshold are based on the scenario, the scenario defining a location of the virtual object relative to a location of another object.