System and method for model based people counting

• US 8,295,545 B2
• Filed: 11/17/2008
• Issued: 10/23/2012
• Est. Priority Date: 11/17/2008
• Status: Active Grant

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Hampapur, Arun, Tian, Ying-li, Zhai, Yun
• Primary Examiner(s): Lu, Tom Y
• Application Number:
  US12/272,122
• Publication Number:
  US 20100124357A1
• Time in Patent Office: 1,436 Days
• Field of Search: 382/103
• US Class Current: 382/103
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30196 Human being; Person
• CPC Class Codes: G06T 7/251 involving models
• CPC Class Codes: G06V 20/53 Recognition of crowd images, e.g. recognition of crowd congestion

Abstract

An approach that allows for model based people counting is provided. In one embodiment, there is a generating tool configured to generate a set of person-shape models based on results of a cumulative training process; a detecting tool configured to detect persons in a camera field-of-view by using the set of person-shape models, and a counting tool configured to track detected persons upon crossing by the detected persons of a previously established virtual boundary.

Claims

1. A method for counting people using overhead camera views, said method comprising:

generating a set of person-shape models during a cumulative training process, the set of person-shape models comprising a shape cue from an overhead view of a head and shoulder region of a human body;

detecting persons in a camera field-of-view by using said set of person-shape models; and

counting people by tracking detected persons upon crossing by said detected persons of a previously established virtual boundary.

2. The method according to claim 1, said generating a set of person-shape models further comprising:

computing a quantized gradient map of an input image; and

deriving a probabilistic map of gradient magnitude for each quantized gradient direction by accumulating person-shape models generated by said cumulative training process.

3. The method according to claim 1, said detecting persons in a camera field-of-view further comprising:

constructing at least one gradient map consisting of a plurality of down-sampled input images and a plurality of up-sampled input images, and

convolving a plurality of constructed gradient maps with said set of predetermined person-shape models.

4. The method according to claim 1, said counting people further comprising:

predicting a position of a person in the next frame based on results of the current said person'"'"'s detection and tracking;

performing detection of said person in the spatial proximity of said predicted position;

performing detection of said person in a broader region around said predicted position if no person is detected in said spatial proximity;

updating tracking results by matching detected person with previously tracked persons; and

increasing count upon crossing virtual boundary by said tracked person.

5. A system for counting people using overhead camera views, said system comprising:

at least one processing unit;

memory operably associated with the at least one processing unit;

a generating tool storable in memory and executable by the at least one processing unit, said generating tool configured to generate a set of person-shape models comprising a shape cue from an overhead view of a head and shoulder region of a human body;

a detecting tool storable in memory and executable by the at least one processing unit, said detecting tool configured to detect persons in a camera field-of-view by using said set of person-shape models, and

a counting tool storable in memory and executable by the at least one processing unit, said counting tool configured to track detected persons upon crossing by said detected persons of a previously established virtual boundary.

6. The generating tool according to claim 5 further comprising:

a computing component configured to compute a quantized gradient map of an input image; and

a deriving component configured to derive a probabilistic map of gradient magnitudes for each quantized gradient direction by accumulating results obtained during the cumulative training process.

7. The detecting tool according to claim 5 further comprising:

a constructing component configured to construct at least one gradient map consisting of a plurality of down-sampled input images and a plurality of up-sampled input images; and

a convolving component configured to convolve a plurality of gradient maps with said set of predetermined person-shape models.

8. The counting tool according to claim 5, further comprising:

a predicting component configured to predict a position of a person in the next frame based on results of the current said person'"'"'s detection and tracking;

a detecting component configured to perform detection of said person in the spatial proximity of said predicted position and in a broader region around said predicted position if no person is detected in said spatial proximity;

an updating component configured to update tracking results by matching detected person with previously tracked persons; and

a counting component configured to increase count upon crossing virtual boundary by said tracked person.

9. A computer-readable storage medium storing computer instructions, which when executed, enables a computer system to count people using overhead camera views, the computer instructions comprising:

generating a set of person-shape models during a cumulative training process, the set of person-shape models comprising a shape cue from an overhead view of a head and shoulder region of a human body;

detecting persons in a camera field-of-view by using said set of person-shape models; and

counting people by tracking detected persons upon crossing by said detected persons of a previously established virtual boundary.

10. The computer-readable storage medium according to claim 9 further comprising computer instructions for:

computing a quantized gradient map of an input image; and

deriving a probabilistic map of gradient magnitude for each quantized gradient direction by accumulating person-shape models generated by said cumulative training process.

11. The computer-readable storage medium according to claim 9 further comprising computer instructions for:

constructing at least one gradient map consisting of a plurality of down-sampled input images and a plurality of up-sampled input images; and

convolving a plurality of constructed gradient maps with said set of predetermined person-shape models.

12. The computer-readable storage medium according to claim 9 further comprising computer instructions for:

predicting a position of a person in the next frame based on results of the current said person'"'"'s detection and tracking;

performing detection of said person in the spatial proximity of said predicted position;

performing detection of said person in a broader region around said predicted position if no person is detected in said spatial proximity;

updating tracking results by matching detected person with previously tracked persons; and

increasing count upon crossing virtual boundary by said tracked person.

13. A method for deploying a counting tool for counting people using overhead camera views, said method comprising:

providing a computer infrastructure operable to;

generate a set of person-shape models comprising a shape cue from an overhead view of a head and shoulder region of a human body;

detect persons in a camera field-of-view by using said set of person-shape models; and

count people by tracking detected persons upon crossing by said detected persons of a previously established virtual boundary.

14. The method according to claim 13, the computer infrastructure further operable to:

compute a quantized gradient map of an input image; and

derive a probabilistic map of gradient magnitude for each quantized gradient direction by accumulating person-shape models generated by said cumulative training process.

15. The method according to claim 13, the computer infrastructure further operable to:

construct at least one gradient map consisting of a plurality of down-sampled input images and a plurality of up-sampled input images; and

convolve a plurality of constructed gradient maps with said set of predetermined person-shape models.

16. The method according to claim 13, the computer infrastructure further operable to:

predict a position of a person in the next frame based on results of the current said person'"'"'s detection and tracking;

perform detection of said person in the spatial proximity of said predicted position;

perform detection of said person in a broader region around said predicted position if no person is detected in said spatial proximity;

update tracking results by matching detected person with previously tracked persons; and

increase count upon crossing virtual boundary by said tracked person.