Data optimization

• US 9,857,869 B1
• Filed: 06/17/2014
• Issued: 01/02/2018
• Est. Priority Date: 06/17/2014
• Status: Active Grant

• Current Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Original Assignee: Amazon Technologies, Inc. (Amazon.com, Inc.)
• Inventors: Doucette, Jason Allen
• Primary Examiner(s): Moon, Seokyun
• Application Number:
  US14/307,113
• Time in Patent Office: 1,295 Days
• Field of Search: None
• CPC Class Codes: G06F 1/1626 with a single-body enclosure integrating a flat display, e.g. Personal Digital Assistants [PDAs]
• CPC Class Codes: G06F 1/1686 the I/O peripheral being an integrated camera
• CPC Class Codes: G06F 3/012 Head tracking input arrangements
• CPC Class Codes: G06F 3/013 Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06F 3/0304 Detection arrangements using opto-electronic means constructional details of pointing devices not related to the detection arrangement using opto-electronic means G06F3/033; optical digitisers G06F3/042
• CPC Class Codes: G06V 40/18 Eye characteristics, e.g. of the iris
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

A service such as an object tracking optimization service of a computing device is configured to optimize the use of object tracking information by, e.g., smoothing, averaging, or otherwise processing the information. Approaches enable the object tracking optimization service to acquire object tracking information (e.g., position information) at a variable input rate and provide the information to applications, services, and the like which require such information at variable rates. The information can be used to display image content (e.g., still or video content) in such a way that the image content will appear, to a viewer, to include portions with different locations in physical space, with the relative positioning of those portions being determined at least in part upon a current relative position and/or orientation of the viewer with respect to the device, as well as changes in that relative position and/or orientation.

Claims

1. A computing device, comprising:

a display screen;

a computing device processor;

a head tracking subsystem configured to generate position information corresponding to a position of a head of a viewer of the computing device relative to the computing device;

a data optimizing component configured to utilize an averaging function to average position information;

a memory device including instructions that, when executed by the computing device processor, cause the computing device to;

acquire the position information by the data optimizing component;

associate the position information with a first weighting factor, the first weighting factor configured to control a responsiveness of displayed content due to a change in the position of the head of the viewer;

acquire previously averaged position information;

associate the previously averaged position information with a second weighting factor, the first weighting factor being different from the second weighting factor;

generate averaged position information by applying the at least one averaging function to the position information, the first weighting factor, the previously averaged position information, and the second weighting factor;

acquire, by a rendering component configured to operate on the computing device, the averaged position information from the data optimizing component; and

render, by the rendering component and based on the averaged position information, content at a variable rate.

2. The computing device of claim 1, wherein the instructions when executed to generate the averaged position information, further cause the computing device to:

acquire image data using a camera of the computing device, the image data including a representation of a portion of the head of the viewer;

determine a confidence value corresponding to how much of the head of the viewer is detected in the image data; and

generate the averaged position information to render the content on the confidence value.

3. The computing device of claim 2, wherein the instructions when executed to determine the confidence value, further cause the computing device to:

determine an amount of the portion of the head represented in the image data; and

determine that the amount of the portion of the head meets at least one of a plurality of thresholds, each threshold of the plurality of thresholds corresponding to a respective confidence value.

4. The computing device of claim 3, wherein the instructions when executed to determine the first weighting factor, further cause the computing device to:

receive a responsiveness metric corresponding to the position information;

receive a penetration percentage corresponding to a level of control of the position information; and

determine the first weighting factor based on the penetration percentage and the responsiveness metric.

5. A computer implemented method, comprising:

receiving first averaged position information that is associated with first position information corresponding to a gaze direction of a user within a first period of time;

receiving a first weighting factor associated with the first averaged position information;

receiving, at a first rate, second position information corresponding to the gaze direction within a second period of time;

receiving a second weighting factor associated with the second position information;

determining second averaged position information by applying an averaging algorithm to the second position information, the second weighting factor, the first averaged position information, and the first weighting factor;

determining a second rate for rendering data representing a view of content that corresponds to the second averaged position information, the second rate being greater in frequency when the gaze direction is substantially towards a specified object than when the gaze direction is substantially away from the specified object; and

rendering the data at the second rate.

6. The computer implemented method of claim 5, further comprising:

determining the second position information from at least one of image data acquired by a camera, sensor information acquired by a motion sensor, or a service configured to provide the second position information, the service providing the second position information based on a combination of first position data determined from the image data and second position data determined from the sensor information.

7. The computer implemented method of claim 6, further comprising:

analyzing the image data to identify feature points;

determining, based on the feature points, a feature vector corresponding to a representation of the user; and

determining the second position information based on the feature vector.

8. The computer implemented method of claim 5, wherein the averaging algorithm includes at least one of an exponential moving average algorithm, or a moving average algorithm.

9. The computer implemented method of claim 5, further comprising:

receiving a responsiveness metric corresponding to a number of frames of the second position information;

receiving a penetration percentage corresponding to a level of control of the number of frames of the second position information; and

determining the second weighting factor based on the penetration percentage and the responsiveness metric.

10. The computer implemented method of claim 5, further comprising:

detecting an amount of movement of the specified object with respect to the user;

determining an amount of acceleration of the specified object based on the amount of movement; and

decreasing the second weighting factor for a period of time the specified object is accelerating.

11. The computer implemented method of claim 5, further comprising:

determining a confidence value corresponding to a level of representation of the user in image data acquired by a camera, wherein the second averaged position information is further based on the confidence value.

12. The computer implemented method of claim 11, wherein the confidence value is based on a number of features detected in the representation of the user, a location of the representation of the user in the image data, and a number of predetermined features detected in the representation of the user.

13. The computer implemented method of claim 5, further comprising:

determining a type of rendering component configured to render the view of content or an amount of graphics rendered on a display screen, wherein the second rate is further based on the type of rendering component or the amount of graphics.

14. The computer implemented method of claim 5, further comprising:

tracking an amount of movement of the user over a set of image data representing a plurality of images;

determining that the amount of movement is below a threshold level of movement; and

generating steady state position information.

15. A computing device, comprising:

a display screen;

a computing device processor; and

a memory device including instructions that, when executed by the computing device processor, cause the computing device to;

receive first averaged position information that is associated with first position information corresponding to a gaze direction of a user within a first period of time;

receive a first weighting factor associated with the first averaged position information;

receive, at a first rate, second position information corresponding to the gaze direction within a second period of time;

receive a second weighting factor associated with the second position information;

determine second averaged position information based by applying an averaging algorithm to the second position information, the second weighting factor, the first averaged position information, and the first weighting factor;

determine a second rate for rendering data representing a view of content that corresponds to the second averaged position information, the second rate being greater in frequency when the gaze direction is substantially towards the computing device than when the gaze direction is substantially away from the computing device; and

render the data at the second rate.

16. The computing device of claim 15, wherein the instructions when executed further cause the computing device to:

determine a type of rendering component configured to render the view of content or an amount of graphics rendered on the display screen, wherein the second rate is further based on the type of rendering component or the amount of graphics.

17. The computing device of claim 15, wherein the computing device further comprises a camera, and wherein the instructions when executed further cause the computing device to:

determine a confidence value corresponding to a level of representation of the user in image data acquired by the camera, wherein the second averaged position information is generated based on the confidence value.

18. The computing device of claim 15, wherein the instructions when executed further cause the computing device to:

determine the second position information from at least one of image data acquired by a camera of the computing device, sensor information acquired by a motion sensor of the computing device, or a service configured to provide the second position information, the service providing the second position information based on a combination of first position data determined from the image data and second position data determined from the sensor information.

19. The computing device of claim 15, wherein the instructions when executed further cause the computing device to:

receive a responsiveness metric corresponding to a number of frames of the second position information;

receive a penetration percentage corresponding to a level of control of the number of frames of the second position information; and

determine the second weighting factor based on the penetration percentage and the responsiveness metric.

20. The computing device of claim 15, wherein the instructions when executed further cause the computing device to:

detect an amount of movement of the computing device with respect to the user;

determine an amount of acceleration of the computing device on the amount of movement; and

decrease the second weighting factor for a period of time the computing device is accelerating.