METHODS, APPARATUS AND ARTICLES OF MANUFACTURE TO DETECT SHAPES

• US 20180124456A1
• Filed: 01/02/2018
• Published: 05/03/2018
• Est. Priority Date: 03/12/2013
• Status: Active Grant

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Terrazas, Alejandro
• Granted Patent: US 10,250,942 B2
• Time in Patent Office: Days
• CPC Class Codes: G06V 20/64 Three-dimensional objects
• CPC Class Codes: G06V 40/10 Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
• CPC Class Codes: H04N 21/42201 biosensors, e.g. heat sensor for presence detection, EEG sensors or any limb activity sensors worn by the user input arrangements for interaction with the human body based on nervous system activity detection G06F3/015
• CPC Class Codes: H04N 21/4223 Cameras H04N23/00 takes precedence
• CPC Class Codes: H04N 21/442 Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk arrangements for monitoring broadcast services or broadcast-related services H04H60/29; arrangements for identifying or recognising characteristics with a direct linkage to broadcast information H04H60/35; monitoring of user activities for profile generation for accessing a video database G06F16/739; monitoring in wireless networks H04W24/00
• CPC Class Codes: H04N 21/44218 Detecting physical presence or behaviour of the user, e.g. using sensors to detect if the user is leaving the room or changes his face expression during a TV program methods or arrangements for recognising human body or animal bodies or body parts G06V40/10; methods or arrangements for acquiring or recognising human faces, facial parts, facial sketches, facial expressions G06V40/16; methods or arrangements for recognising movements or behaviour G06V40/20; arrangements for identifying users in broadcast systems H04H60/45
• CPC Class Codes: H04N 23/45 for generating image signals from two or more image sensors being of different type or operating in different modes, e.g. with a CMOS sensor for moving images in combination with a charge-coupled device [CCD] for still images
• CPC Class Codes: H04N 5/33 Transforming infrared radiation cameras or camera modules for generating image signals from infrared radiation H04N23/20; circuitry of SSIS for transforming infrared radiation into image signals H04N25/20

Abstract

Methods, apparatus, and articles of manufacture to detect shapes are disclosed. Example methods disclosed herein include determining a first likelihood of detection for a reference shape at a first distance from an image capturing device, and storing the reference shape in a first group of a plurality of groups in the database based on a comparison of the first likelihood of detection with a first threshold, the first group associated with the first distance. Disclosed examples also include selecting reference shapes of the first group to compare to an object outline detected in an image in response to a query, the query including a depth value associated with the detected object outline, the selecting of the reference shapes of the first group based on the depth value.

Claims

1. An apparatus to manage a database for object recognition, the apparatus comprising:

a detector to determine a first likelihood of detection for a reference shape at a first distance from an image capturing device;

a grouper to store the reference shape in a first group of a plurality of groups in the database based on a comparison of the first likelihood of detection with a first threshold, the first group associated with the first distance; and

a selector to select reference shapes of the first group to compare to an object outline detected in an image in response to a query, the query including a depth value associated with the detected object outline, the selector to select the reference shapes of the first group based on the depth value.

2. The apparatus of claim 1, wherein the detector is to determine a second likelihood of detection for the reference shape at a second distance from the image capturing device different than the first distance, and the grouper is to store the reference shape in a second group of the plurality of groups when the second likelihood of detection is greater than a second threshold, the second group associated with the second distance.

3. The apparatus of claim 1, wherein respective ones of the plurality of groups in the database are associated with respective different distances from the image capturing device, and the grouper is to store the reference shape in ones of the plurality of groups in the database based on comparisons of respective likelihoods of detection at the respective distances with respective thresholds associated with the respective distances, a first one of the respective thresholds different than a second one of the respective thresholds.

4. The apparatus of claim 1, wherein the grouper is to determine a type of the reference shape, and the first group is also associated with the type of the reference shape.

5. The apparatus of claim 4, wherein the types of the reference shape includes at least one of a standing person, a head, a chair, and a combination of a person and a couch.

6. The apparatus of claim 1, wherein the detector is to determine the first likelihood of detection based on at least one of a size of the reference shape or input from an administrator.

7. The apparatus of claim 1, wherein the detector is to determine the first likelihood of detection based on a Bayesian network.

8. A tangible machine readable storage medium comprising instructions that, when executed, cause a processor to at least:

determine a first likelihood of detection for a reference shape at a first distance from an image capturing device;

store the reference shape in a first group of a plurality of groups in the database based on a comparison of the first likelihood of detection with a first threshold, the first group associated with the first distance; and

select reference shapes of the first group to compare to an object outline detected in an image in response to a query, the query including a depth value associated with the detected object outline, the selecting of the reference shapes of the first group based on the depth value.

9. The storage medium of claim 8, where in the instruction further cause the processor to determine a second likelihood of detection for the reference shape at a second distance from the image capturing device different than the first distance, and store the reference shape in a second group of the plurality of groups when the second likelihood of detection is greater than a second threshold, the second group associated with the second distance.

10. The storage medium of claim 8, wherein respective ones of the plurality of groups in the database are associated with respective different distances from the image capturing device, and the instruction further cause the processor to store the reference shape in ones of the plurality of groups in the database based on comparisons of respective likelihoods of detection at the respective distances with respective thresholds associated with respective distances, a first one of the respective thresholds different than a second one of the respective thresholds.

11. The storage medium of claim 8, wherein the instructions further cause the processor to determine a type of the reference shape, and the first group is also associated with the type of the reference shape.

12. The storage medium of claim 11, wherein the types of the reference shape include at least one of a standing person, a head, a chair, and a combination of a person and a couch.

13. The storage medium of claim 8, wherein the instructions further cause the processor to determine the first likelihood of detection based on at least one of a size of the reference shape or input from an administrator.

14. The storage medium of claim 8, wherein the instructions further cause the processor to determine the first likelihood of detection based on a Bayesian network.

15. A method comprising:

determining, by executing an instruction with a processor, a first likelihood of detection for a reference shape at a first distance from an image capturing device;

storing the reference shape in a first group of a plurality of groups in the database based on a comparison of the first likelihood of detection with a first threshold, the first group associated with the first distance; and

selecting, by executing an instruction with the processor, reference shapes of the first group to compare to an object outline detected in an image in response to a query, the query including a depth value associated with the detected object outline, the selecting of the reference shapes of the first group based on the depth value.

16. The method of claim 15, further including determining a second likelihood of detection for the reference shape at a second distance from the image capturing device different than the first distance, and store the reference shape in a second group of the plurality of groups when the second likelihood of detection is greater than a second threshold, the second group associated with the second distance.

17. The method of claim 15, further including determining a type of the reference shape and associating the first group with the type of the reference shape.

18. The method of claim 17, wherein the types of the reference shapes include at least one of a standing person, a head, a chair, and a combination of a person and a couch.

19. The method of claim 15, further including determining the first likelihood of detection based on at least one of a size of the reference shape or input from an administrator.

20. The method of claim 15, further including determining the first likelihood of detection based on a Bayesian network.