Methods and apparatus to count people

• US 9,986,289 B2
• Filed: 03/02/2015
• Issued: 05/29/2018
• Est. Priority Date: 03/02/2015
• Status: Active Grant

• Current Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Original Assignee: The Nielsen Company LLC (MOOD Media Corporation)
• Inventors: Soundararajan, Padmanabhan, Usaj, Marko, Srinivasan, Venugopal
• Primary Examiner(s): Pendleton, Brian T
• Assistant Examiner(s): CONTINO SAUMBY, KRISTA
• Application Number:
  US14/635,360
• Publication Number:
  US 20160261911A1
• Time in Patent Office: 1,184 Days
• CPC Class Codes: G01B 3/006 having both coarse and fine graduation
• CPC Class Codes: G01B 3/20 Slide gauges
• CPC Class Codes: G01B 5/30 for measuring the deformation in a solid, e.g. mechanical strain gauge
• CPC Class Codes: G01C 11/025 by scanning the object
• CPC Class Codes: G02B 26/10 Scanning systems
• CPC Class Codes: G02B 27/0093 with means for monitoring data relating to the user, e.g. head-tracking, eye-tracking
• CPC Class Codes: G04F 3/06 with electric driving mechanisms
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06K 7/06 by means which conduct current when a mark is sensed or absent, e.g. contact brush for a conductive mark
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06V 20/53 Recognition of crowd images, e.g. recognition of crowd congestion
• CPC Class Codes: G06V 20/59 inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation
• CPC Class Codes: G06V 40/173 face re-identification, e.g. recognising unknown faces across different face tracks
• CPC Class Codes: G09B 5/02 with visual presentation of the material to be studied, e.g. using film strip
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions
• CPC Class Codes: H04H 60/45 for identifying users
• CPC Class Codes: H04N 17/00 Diagnosis, testing or measuring for television systems or their details
• CPC Class Codes: H04N 21/25891 being end-user preferences retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18
• CPC Class Codes: H04N 21/4223 : Cameras H04N23/00 takes precedence
• CPC Class Codes: H04N 21/4334 : Recording operations recording of a television signal H04N5/76; arrangements for recording or accumulating broadcast information or broadcast-related information H04H60/27
• CPC Class Codes: H04N 21/44008 : involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream arrangements characterised by components specially adapted for monitoring, identification or recognition of video in broadcast systems H04H60/59
• 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 21/44222 : Analytics of user selections, e.g. selection of programs or purchase activity monitoring of user selections in data processing systems G06F11/34; arrangements for monitoring the user's behaviour or opinions in broadcast systems H04H60/33
• CPC Class Codes: H04N 21/44224 : Monitoring of user activity on external systems, e.g. Internet browsing
• CPC Class Codes: H04N 21/6582 : Data stored in the client, e.g. viewing habits, hardware capabilities, credit card number arrangements where receivers interact with the broadcast H04H20/38
• CPC Class Codes: H04N 3/09 : for electromagnetic radiation in the invisible region, e.g. infrared
• 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
• CPC Class Codes: H04N 5/76 : Television signal recording
• CPC Class Codes: H04N 5/91 : Television signal processing therefor
• CPC Class Codes: H04N 5/93 : Regeneration of the television signal or of selected parts thereof
• CPC Class Codes: H04N 7/125 : the signal segment being a picture element
• CPC Class Codes: H04N 7/188 : Capturing isolated or intermittent images triggered by the occurrence of a predetermined event, e.g. an object reaching a predetermined position signal generation from motion picture films H04N5/253
• CPC Class Codes: H04N 9/24 : using means, integral with, or external to, the tube, for producing signal indicating instantaneous beam position
• CPC Class Codes: H04N 9/87 : Regeneration of colour television signals H04N9/80 takes precedence

Abstract

Methods and apparatus to count people are disclosed. An example apparatus includes a populator to populate a list with first characteristic datasets obtained from first image data representative of an environment during a first period of time, respective ones of the first characteristic datasets representative of a face detected in the environment during the first period of time; a comparator to compare the first characteristic datasets to each other to determine a first number of unique faces in the environment during the first period of time; and a discarder to delete the first characteristic datasets from the list when the first period of time has ended, the populator to re-populate the list with second characteristic datasets obtained from second image data representative of the environment during a second period of time subsequent to the first period of time.

Claims

1. An apparatus comprising:

a discarder to delete existing characteristic datasets from a list;

a populator to populate the list with first characteristic datasets obtained from a first plurality of images representative of an environment during a first period of time of a media presentation, respective ones of the first characteristic datasets representative of corresponding faces detected in the environment during the first period of time;

a limiter to limit a number of the first characteristic datasets stored in the list for a first location represented in the first plurality of images;

a comparator to compare the first characteristic datasets to each other to determine a first number of unique faces in the environment during the first period of time, the comparator to limit the comparisons of the respective first characteristic datasets to other first characteristic datasets obtained during the first period of time; and

the discarder to delete the first characteristic datasets from the list after the first period of time has ended, the populator to re-populate the list with second characteristic datasets obtained from a second plurality of images representative of the environment during a second period of time of the media presentation, subsequent to the first period of time, and the discarder, the populator, the limiter and the comparator implemented in a circuit.

2. The apparatus as defined in claim 1, wherein the apparatus is not to query a facial recognition database to determine the first number of unique faces in the environment.

3. The apparatus as defined in claim 2, wherein the facial recognition database is separately implemented from the list.

4. The apparatus as defined in claim 1, wherein the comparator is to compare the second characteristic datasets to each other to determine a second number of unique faces in the environment during the second period of time, the comparator to limit the comparison of the respective second characteristic datasets to other ones of the second characteristic datasets obtained during the second period of time.

5. The apparatus as defined in claim 1, further including a time period manager, implemented in a circuit, to:

trigger the comparator to compare the first characteristic datasets to each other after the first period of time has ended; and

trigger the discarder to delete the first characteristic datasets from the list after the first period of time has ended.

6. The apparatus as defined in claim 1, further including a media detector to obtain media identifying information during the first period of time.

7. The apparatus as defined in claim 6, further including a reporter to generate a data package including the media identifying information detected during the first period of time and the first number of unique faces determined by the comparator to be in the environment during the first period of time.

8. The apparatus as defined in claim 1, further including a face detector to detect faces in the first image data, the first characteristic datasets being obtained from locations generated by the face detector.

9. The apparatus as defined in claim 1, further including a characteristic dataset obtainer to obtain the first and second characteristic datasets, the first and second characteristic datasets including facial feature vectors.

10. A method comprising:

deleting, by executing an instruction with a processor, existing characteristic datasets from a list;

obtaining, by executing an instruction with the processor, first characteristic datasets for corresponding faces detected in a first plurality of images representative of an environment over a first period of time of a media presentation;

populating, by executing an instruction with the processor, the list with the first characteristic datasets;

limiting, by executing an instruction with the processor, a number of the first characteristic datasets stored in the list for a particular location represented in the first plurality of images to an upper limit, the limiting to reduce an amount of memory used to store the list; and

after the first period of time expires;

comparing, by executing an instruction with the processor, the first characteristic datasets to each other to determine a uniqueness of respective ones of the first characteristic datasets, the comparing limited to comparisons of the respective first characteristic datasets to other ones of the first characteristic datasets obtained during the first period of time;

deleting, by executing an instruction with the processor, the first characteristic datasets from the list; and

storing, in a memory, a first people count for the first period of time based on the comparing of the first characteristic datasets to each other.

11. The method as defined in claim 10, further including obtaining, by executing an instruction with the processor, second characteristic datasets for corresponding faces detected in a second plurality of images representative of the environment over a second period of time of the media presentation, the second period of time subsequent to the first time.

12. The method as defined in claim 11, further including:

populating, by executing an instruction with the processor, the list with the second characteristic datasets; and

when the second period of time expires;

comparing, by executing an instruction with the processor, the second characteristic datasets to each other to determine a uniqueness of respective ones of the second characteristic datasets, the comparing limited to comparisons of the respective second characteristic datasets to other ones of the second characteristic datasets obtained during the second period of time;

deleting, by executing an instruction with the processor, the second characteristic datasets from the list; and

storing, in the memory, a second people count for the second period of time based on the comparing of the second characteristic datasets to each other.

13. The method as defined in claim 10, wherein the comparing of the first characteristic datasets to each other includes not comparing the first characteristic datasets to data of a facial recognition database.

14. The method as defined in claim 10, further including obtaining media identifying information during the first period of time.

15. The method as defined in claim 14, further including generating a data package including the media identifying information detected during the first period of time and the first people count.

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

delete existing characteristic datasets from a list;

populate the list with first characteristic datasets obtained from a first plurality of images representative of an environment during a first period of time of a media presentation, respective ones of the first characteristic datasets representative of respective faces detected in the environment during the first period of time;

limit a number of the first characteristic datasets stored in the list for a first location in respective ones of the first plurality of images to not exceed an upper limit;

compare the first characteristic datasets to each other to determine a first number of unique faces in the environment during the first period of time, the comparing limited to comparisons of the respective first characteristic datasets to other ones of the first characteristic datasets obtained during the first period of time;

delete the first characteristic datasets from the list after the first period of time has ended; and

re-populate the list with second characteristic datasets obtained from a second plurality of images representative of the environment during a second period of time of the media presentation, the second period of time subsequent to the first period of time.

17. The storage medium as defined in claim 16, wherein the machine is to not query a facial recognition database to determine the first number of unique faces in the environment.

18. The storage medium as defined in claim 16, wherein the instructions, when executed, cause the machine to compare the second characteristic datasets to each other to determine a second number of unique faces in the environment during the second period of time, the comparing limited to comparisons of the respective second characteristic datasets to other ones of the second characteristic datasets obtained during the second period of time.

19. The storage medium as defined in claim 16, wherein the machine is to not compare the first characteristic datasets to the second characteristic datasets.

20. The storage medium as defined in claim 16, wherein the instructions, when executed, cause the machine to:

trigger the comparing of the first characteristic datasets to each other after the first period of time has ended; and

trigger the deleting of the first characteristic datasets from the list after the first period of time has ended.

21. The storage medium as defined in claim 16, wherein the instructions, when executed, cause the machine to obtain media identifying information during the first period of time.

22. The storage medium as defined in claim 21, wherein the instructions, when executed, cause the machine to generate a data package including the media identifying information detected during the first period of time and the number of unique faces in the environment during the first period of time.

23. The storage medium as defined in claim 16, wherein the instructions, when executed, cause the machine to detect faces in the first image data and locations of the faces, the first characteristic datasets being obtained from the locations.

24. The storage medium as defined in claim 16, wherein the instructions, when executed, cause the machine to obtain the first and second characteristic datasets, the first and second characteristic datasets including facial feature vectors.