Generating Furtive Glance Cohorts from Video Data

• US 20100153597A1
• Filed: 12/15/2008
• Published: 06/17/2010
• Est. Priority Date: 12/15/2008
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Kraemer, James R., Angell, Robert Lee, Friedlander, Robert R.
• Application Number:
  US12/335,521
• Publication Number:
  US 20100153597A1
• Time in Patent Office: Days
• US Class Current: 710/69
• CPC Class Codes: G06F 16/7867 using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 40/174 Facial expression recognition

Abstract

The illustrative embodiments described herein provide a computer implemented method, apparatus, and computer program product for generating furtive glance cohorts. In an illustrative embodiment, video data of a monitored population is received and processed to form digital video data. The digital video data includes metadata describing glancing patterns associated with one or more subjects from the monitored population. The digital video is analyzed to identify a set of furtive glance patterns from the glancing patterns. One or more furtive glance attributes for the set of furtive glance cohorts are selected from the set of furtive glance patterns. Thereafter, the set of furtive glance cohorts is generated. The set of furtive glance cohorts have members selected from the monitored population and have at least one furtive glance attribute in common.

Claims

1. A computer implemented method for generating a set of furtive glance cohorts, the computer implemented method comprising:

responsive to receiving video data of a monitored population, processing the video data to form digital video data, wherein the digital video data comprises metadata describing glancing patterns associated with one or more subjects from the monitored population;

analyzing the digital video data to identify a set of furtive glance patterns from the glancing patterns, wherein one or more furtive glance attributes for the set of furtive glance cohorts are selected from the set of furtive dance patterns; and

generating the set of furtive glance cohorts comprising members selected from the monitored population, wherein each member of a cohort in the set of furtive glance cohorts has at least one furtive glance attribute in common.

2. The computer implemented method of claim 1, wherein processing the digital video data further comprises:

processing the video data in a set of data models for identifying glancing patterns in the video data.

3. The computer implemented method of claim 1, wherein analyzing the digital video data further comprises:

comparing the glancing patterns to a set of historic glancing patterns to identify the set of furtive glance patterns.

4. The computer implemented method of claim 1, wherein generating the set of furtive glance cohorts further comprises:

identifying a set of furtive glance attributes from the set of furtive glance patterns, wherein the set of furtive glance attributes is identified from one or more set of furtive glance patterns using cohort criteria.

5. The computer implemented method of claim 1, wherein the set of furtive glance patterns comprises at least one of a threshold rate of eye movement, a threshold rate of head movement, a number of objects viewed in a predefined period of time, and a number of times an object is viewed.

6. The computer implemented method of claim 1 further comprising:

generating inferences using the set of furtive glance cohorts, wherein the inferences indicate a possible future action taken by the members of the set of furtive glance cohorts.

7. The computer implemented method of claim 1 further comprising:

updating historical furtive glance patterns with patterns in the set of furtive glance patterns present in the digital video data.

8. A computer program product for generating furtive glance cohorts, the computer program product comprising:

a computer recordable-type medium;

first program instructions for processing the video data to form digital video data in response to receiving video data of a monitored population, wherein the digital video data comprises metadata describing glancing patterns associated with one or more subjects from the monitored population;

second program instructions for analyzing the digital video data to identify a set of furtive glance patterns from the glancing patterns, wherein one or more furtive glance attributes for the set of furtive glance cohorts are selected from the set of furtive glance patterns;

third program instructions for generating the set of furtive glance cohorts comprising members selected from the monitored population, wherein each member of a cohort in the set of furtive glance cohorts has at least one furtive glance attribute in common; and

wherein the first program instructions, the second program instructions, and the third program instructions are stored on the computer recordable-type medium.

9. The computer program product of claim 8, wherein the first program instructions further comprise program instructions for processing the video data in a set of data models for identifying glancing patterns in the video data.

10. The computer program product of claim 8, wherein the second program instructions further comprises instructions for comparing the glancing patterns to a set of historic glancing patterns to identify the set of furtive glance patterns.

11. The computer program product of claim 8, wherein the third program instructions further comprise program instructions for identifying a set of furtive glance attributes from the set of furtive glance patterns, wherein the set of furtive glance attributes is identified from one or more set of furtive glance patterns using cohort criteria.

12. The computer program product of claim 8 wherein the set of furtive glance patterns comprises at least one of a threshold rate of eye movement, a threshold rate of head movement, a number of objects viewed in a predefined period of time, and a number of times an object is viewed.

13. The computer program product of claim 8 further comprising:

fourth program instructions for generating inferences using the set of furtive glance cohorts, wherein the inferences indicate a possible future action taken by the members of the set of furtive glance cohorts, and wherein the fourth program instructions are stored on the computer recordable-type medium.

14. The computer program product of claim 8, further comprising:

fifth program instructions for updating historical furtive glance patterns with patterns in the set of furtive glance patterns present in the digital video data, wherein the fifth program instructions are stored on the computer recordable-type medium.

15. An apparatus for generating furtive glance cohorts, the apparatus comprising:

a bus system;

a memory connected to the bus system, wherein the memory includes computer usable program code; and

a processing unit connected to the bus system, wherein the processing unit executes the computer usable program code to process the video data to form digital video data in response to receiving video data of a monitored population, wherein the digital video data comprises metadata describing glancing patterns associated with one or more subjects from the monitored population;

analyze the digital video data to identify a set of furtive glance patterns from the glancing patterns, wherein one or more furtive glance attributes for the set of furtive glance cohorts are selected from the set of furtive glance patterns; and

generate the set of furtive glance cohorts comprising members selected from the monitored population, wherein each member of a cohort in the set of furtive glance cohorts has at least one furtive glance attribute in common.

16. The apparatus of claim 15, wherein the processing unit further executes the computer usable program code to process the video data in a set of data models for identifying glancing patterns in the video data.

17. The apparatus of claim 15, wherein the processing unit further executes the computer usable program code to compare the glancing patterns to a set of historic glancing patterns to identify the set of furtive glance patterns.

18. The apparatus of claim 15, wherein the processing unit further executes the computer usable program code to identify a set of furtive glance attributes from the set of furtive glance patterns, wherein the set of furtive glance attributes is identified from one or more set of furtive glance patterns using cohort criteria

19. A system for generating furtive glance cohorts, the system comprising:

a set of sensors, wherein the set of sensors captures video data, wherein the video data comprises glancing patterns;

a pattern processing engine, wherein the pattern processing engine forms digital video data from the patient care data; and

a cohort generation engine, wherein the cohort generation engine generates a set of furtive cohorts from the digital video data, wherein each member in the set of furtive glance cohorts share at least one furtive glance attribute in common.

20. The system of claim 19, further comprising:

an inference engine, wherein the inference engine generates inferences using the set of furtive glance cohorts, wherein the inferences indicate a possible future action taken by the members of the set of furtive glance cohorts.