Anomaly detection in a video system

• US 20080031491A1
• Filed: 08/03/2006
• Published: 02/07/2008
• Est. Priority Date: 08/03/2006
• Status: Abandoned Application

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Bazakos, Michael E., Ma, Yunqian, Au, Kwong Wing
• Application Number:
  US11/498,923
• Publication Number:
  US 20080031491A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06F 18/2433 Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
• 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: G08B 13/19613 Recognition of a predetermined image pattern or behaviour pattern indicating theft or intrusion

Abstract

In an embodiment, a video processor is configured to identify anomalous or abnormal behavior. A hierarchical behavior model based on the features of the complement of the abnormal behavior of interest is developed. For example, if the abnormal behavior is stealing or shoplifting, a model is developed for the actions of normal shopping behavior (i.e., not stealing or not shoplifting). Features are extracted from video data and applied to an artificial intelligence construct such as a dynamic Bayesian network (DBN) to determine if the normal behavior is present in the video data (i.e, the complement of the abnormal behavior). If the DBN indicates that the extracted features depart from the behavior model (the complement of the abnormal behavior), then the presence of the abnormal behavior in the video data may be assumed.

Claims

1. A system comprising one or more modules to:

receive video data from an environment;

extract features from the received video data;

compare the extracted features from the received video data to a model of a complement of an abnormal behavior; and

deduce that the abnormal behavior is present in the received video data when the comparison departs from the model of the complement of the abnormal behavior.

2. The system of claim 1, wherein the module to compare the extracted features and the complement model includes a dynamic Bayesian network.

3. The system of claim 1, further comprising a module to generate an alert when the extracted features do not correlate with the complement model.

4. The system of claim 1, wherein the complement model comprises a person shopping for items.

5. The system of claim 4, wherein the complement model and the received video data originate in a store environment.

6. The system of claim 4, wherein the complement model includes one or more of:

reaching for an item on a shelf;

examining the item; and

returning the item to the shelf.

7. The system of claim 4, wherein the complement model includes one or more of:

reaching for an item on a shelf;

examining the item; and

placing the item in a shopping cart or basket.

8. The system of claims 6 or 7, further comprising a module to identify an item in a hand of the shopper.

9. A process comprising:

configuring a video processor to;

receive video data from an environment;

extract features from the received video data;

compare the extracted features from the received video data to a model of a complement of an abnormal behavior; and

deduce that the abnormal behavior is present in the received video data when the comparison departs from the model of the complement of the abnormal behavior.

10. The process of claim 9, wherein the wherein the comparison of the extracted features and the complement model includes a dynamic Bayesian network.

11. The process of claim 9, further comprising configuring the video processor to generate an alert when the video processor deduces that the abnormal behavior is present in the received video data.

12. The process of claim 9, wherein the video data includes a person in a shopping environment, and further wherein the abnormal behavior comprises an action relating to a theft of an item.

13. The process of claim 12, wherein the extracted features from the received video data relate to a person removing an item from a store shelf, a person examining the item, a person returning the item to the store shelf, and a person placing the item in a shopping cart.

14. A machine readable medium comprising instructions that when executed by a processor executes a process comprising:

receiving video data from an environment;

extracting features from the received video data;

comparing the extracted features from the received video data to a model of a complement of an abnormal behavior; and

deducing that the abnormal behavior is present in the received video data when the comparison departs from the model of the complement of the abnormal behavior.

15. The machine readable medium of claim 14, wherein the comparison of the extracted features and the complement model includes a dynamic Bayesian network.

16. The machine readable medium of claim 14, further comprising instructions to generate an alert when the extracted features do not correlate with the complement model.

17. The machine readable medium of claim 14, wherein the complement model comprises a person shopping for items.

18. The machine readable medium of claim 17, wherein the complement model and the received video data originate in a store environment.

19. The machine readable medium of claim 17, wherein the complement model includes one or more of:

reaching for an item on a shelf;

examining the item; and

returning the item to the shelf.

20. The machine readable medium of claim 17, wherein the complement model includes one or more of:

reaching for an item on a shelf;

examining the item; and

placing the item in a shopping cart or basket.