Alert volume normalization in a video surveillance system

• US 9,349,275 B2
• Filed: 03/15/2013
• Issued: 05/24/2016
• Est. Priority Date: 03/15/2012
• Status: Active Grant

• Current Assignee: Intellective Ai, Inc.
• Original Assignee: Behavioral Recognition Systems Incorporated
• Inventors: Saitwal, Kishor Adinath, Cobb, Wesley Kenneth
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): LAMARDO, VIKER ALEJANDRO
• Application Number:
  US13/836,730
• Publication Number:
  US 20130241730A1
• Time in Patent Office: 1,166 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06F 18/41 Interactive pattern learning with a human teacher
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/0409 Adaptive resonance theory [ART] networks
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06V 10/7788 the supervisor being a human, e.g. interactive learning with a human teacher
• 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 20/53 Recognition of crowd images, e.g. recognition of crowd congestion
• CPC Class Codes: G06V 40/103 Static body considered as a whole, e.g. static pedestrian or occupant recognition
• CPC Class Codes: G08B 13/19608 Tracking movement of a target, e.g. by detecting an object predefined as a target, using target direction and or velocity to predict its new position
• CPC Class Codes: G08B 21/182 Level alarms, e.g. alarms responsive to variables exceeding a threshold
• CPC Class Codes: G08B 23/00 Alarms responsive to unspecified undesired or abnormal conditions
• CPC Class Codes: G08B 29/185 Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
• CPC Class Codes: H04N 7/002 Special television systems not provided for by H04N7/007 - H04N7/18 still pictures via a television channel H04N1/00098

Abstract

Techniques are disclosed for normalizing and publishing alerts using a behavioral recognition-based video surveillance system configured with an alert normalization module. Certain embodiments allow a user of the behavioral recognition system to provide the normalization module with a set of relative weights for alert types and a maximum publication value. Using these values, the normalization module evaluates an alert and determines whether its rareness value exceed a threshold. Upon determining that the alert exceeds the threshold, the module normalizes and publishes the alert.

Claims

1. A method for normalizing and publishing alerts generated by a behavioral recognition system evaluating a sequence of video frames, the method comprising:

receiving an alert having a type and an original rareness value, wherein the alert corresponds to an observed behavior of one or more foreground objects detected in the sequence of video frames and wherein the original rareness value reflects how common the alert is based on alerts of the type that have previously been generated by the behavioral recognition system while evaluating the sequence of video frames;

converting the original rareness value to an alert percentile value;

upon determining that the alert percentile value is greater than a percentile threshold, normalizing the alert, wherein the percentile threshold is

2. The method of claim 1, wherein the percentile threshold is

3. The method of claim 1, wherein normalizing the alert further comprises calculating a normalized rareness value

4. The method of claim 3, wherein the normalized rareness value is

5. The method of claim 1, wherein publishing the alert further comprises calculating a publication rank value β

=min(P, N_p)*(1−

η

_i), where P is a maximum publication value, N_p is a rank-renormalization constant, and η

_i is a normalization rareness value.

6. The method of claim 5, further comprising upon determining that the publication rank value is less than a maximum dispatch value, dispatching the alert.

7. A computer-readable storage medium storing instructions, which, when executed on a processor, performs an operation for normalizing and publishing an alert generated by a behavioral recognition system evaluating a sequence of video frames, the operation comprising:

receiving an alert having a type and an original rareness value, wherein the alert corresponds to an observed behavior of one or more foreground objects detected in the sequence of video frames and wherein the original rareness value reflects how common the alert is based on alerts of the type that have previously been generated by the behavioral recognition system while evaluating the sequence of video frames;

converting the original rareness value to an alert percentile value;

upon determining that the alert percentile value is greater than a percentile threshold, normalizing the alert, wherein the percentile threshold is

8. The computer-readable storage medium of claim 7,

wherein the percentile threshold is

9. The computer-readable storage medium of claim 7, wherein normalizing the alert further comprises calculating a normalized rareness value

10. The computer-readable storage medium of claim 9, wherein the normalized rareness value is

11. The computer-readable storage medium of claim 7, wherein publishing the alert further comprises calculating a publication rank value β

=min(P, N_p)*(1−

η

_i), where is a maximum publication value, N_p is a rank-renormalization constant, and η

_i is a normalization rareness value.

12. The computer-readable storage medium of claim 11, further comprising upon determining that the publication rank value is less than a maximum dispatch value, dispatching the alert.

13. A system comprising:

a processor; and

a memory for hosting an application, which, when executed on the processor, performs an operation for normalizing and publishing an alert generated by a behavioral recognition system evaluating a sequence of video frames, the operation comprising;

receiving an alert having a type and an original rareness value, wherein the alert corresponds to an observed behavior of one or more foreground objects detected in the sequence of video frames and wherein the original rareness value reflects how common the alert is based on alerts of the type that have previously been generated by the behavioral recognition system while evaluating the sequence of video frames,

converting the original rareness value to an alert percentile value,

upon determining that the alert percentile value is greater than a percentile threshold, normalizing the alert, wherein the percentile threshold is

14. The system of claim 13, wherein the percentile threshold is

15. The system of claim 13, wherein normalizing the alert further comprises calculating a normalized rareness value

16. The system of claim 15, wherein the normalized rareness value is

17. The system of claim 13, wherein publishing the alert further comprises calculating a publication rank value β

=min(P, N_p)*(1−

η

_i), where P is a maximum publication value, N_p is a rank-renormalization constant, and η

_i is a normalization rareness value.

18. The system of claim 17, further comprising upon determining that the publication rank value is less than a maximum dispatch value, dispatching the alert.