EVENT DETECTION BASED ON VIDEO METADATA

• US 20080218591A1
• Filed: 08/24/2007
• Published: 09/11/2008
• Est. Priority Date: 03/06/2007
• Status: Active Grant

• Current Assignee: Cognyte Technologies Israel Ltd. (Cognyte Software Ltd.)
• Original Assignee: Verint Americas Incorporated (Verint Systems Incorporated)
• Inventors: Heier, Kurt, Legendre, Jon, Shoaf, Derek, O'dell, Josh
• Granted Patent: US 9,269,244 B2
• Time in Patent Office: Days
• US Class Current: 348/150
• CPC Class Codes: G06Q 20/20 Point-of-sale [POS] network systems
• CPC Class Codes: G06Q 30/0185 Product, service or business identity fraud
• CPC Class Codes: G06V 20/41 Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items segmenting video sequences G06V20/49
• CPC Class Codes: G06V 20/44 Event 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: G06V 40/103 Static body considered as a whole, e.g. static pedestrian or occupant recognition
• CPC Class Codes: G07G 1/14 Systems including one or more distant stations co-operating with a central processing unit data transmission in general H04L; telemetry systems for selectively calling a substation from a main station H04Q9/00
• CPC Class Codes: G07G 3/00 Alarm indicators, e.g. bells
• CPC Class Codes: G07G 3/003 Anti-theft control theft alarms G08B13/00
• CPC Class Codes: G08B 13/19671 Addition of non-video data, i.e. metadata, to video stream

Abstract

Video data from sources, such as cameras, is analyzed to create metadata descriptive of the events and objects occurring in the video. This metadata, which consists of data about the video, is then analyzed on a transaction basis to determine if a suspicious activity, such as a fraudulent Point of Sale (POS) return event, has occurred in relation to a transaction.

Claims

1. A system for analyzing video metadata relating to one or more transactions at a point-of-sale (POS) location, the system comprising one or more processors and one or more storage devices cooperating to:

receive a feed of video metadata containing information about a video stream corresponding to events occurring at a POS;

receive POS data relating to transactions occurring at the POS;

select and execute one or more of a plurality of or metadata analysis techniques on the video metadata and POS data to identify one or more activities of interest at a POS location.

2. A system according to claim 1, wherein the metadata includes information descriptive of objects and events in the video at the POS.

3. A system according to claim 2, wherein the metadata includes information relating to one or more of:

position, relative size, and movements of an object detected in the video.

4. A system according to claim 3, wherein the activities of interest for a particular POS transaction include the presence or absence of a customer at a POS location associated with the customer, during a time window corresponding to a return transaction.

5. A system according to claim 4, wherein a detection of the absence of a customer at the POS, location associated with the customer is an indication of fraud, the system further comprising a poster application that posts a report of fraud to the POS location upon a detection of fraud.

6. A system according to claim 5, wherein the metadata analysis techniques include one or more of:

(a) an inclusion technique that, for a particular transaction, returns an indication of no fraud if an object in the video metadata, during a time window associated with the transaction, is determined to overlap with a customer POS region by more than a predetermine percentage of the customer region; and

(b) a mutual exclusion technique that, for a particular transaction returns an indication of fraud if an object present in a cashier POS region overlaps the customer POS region by more than a predetermined percentage.

7. A system according to claim 6, wherein objects associated with transactions from a first return lane are analyzed using a first technique, and objects associated with transactions associated with a second return lane are analyzed using a second technique.

8. A system according to claim 7, wherein the first technique is the inclusion technique and the second technique is the mutual exclusion technique.

9. A system according to claim 6, wherein objects associated with transactions recorded from a first camera are analyzed using a first technique, and objects associated with transactions recorded from a second camera are analyzed using a second technique.

10. A system according to claim 9, wherein the first technique is the inclusion technique and the second technique is the mutual exclusion technique.

11. A method for analyzing video metadata relating to one or more transactions at a point-of-sale (POS) location, said method, on a system comprising one or more processors and one or more storage devices, comprising:

receiving a feed of video metadata containing information about a video stream corresponding to events occurring at a POS;

receiving POS data relating to transactions occurring at the POS;

selecting and executing one or more of a plurality of metadata analysis techniques on the video metadata and POS data to identify one or more activities of interest at a POS location.

12. A method according to claim 11, wherein the metadata includes information descriptive of objects and events in the video at the POS.

13. A method according to claim 12, wherein the metadata includes information relating to one or more of:

position, relative size, and movements of an object detected in the video.

14. A method according to claim 13, wherein the activities of interest for a particular POS transaction include the presence or absence of a customer at a POS location associated with the customer, during a time window corresponding to a return transaction.

15. A method according to claim 14, wherein a detection of the absence of a customer at the POS location associated with the customer is an indication of fraud, the system further comprising a poster application that posts a report of fraud to the POS location upon a detection of fraud.

16. A method according to claim 15, wherein the metadata analysis techniques include one or more of:

(a) an inclusion technique that, for a particular transaction, returns an indication of no fraud if an object in the video metadata, during a time window associated with the transaction, is determined to overlap with a, customer POS region by more than a predetermine percentage of the customer region; and

(b) a mutual exclusion technique that, for a particular transaction, returns an indication of fraud if an object present in a cashier POS region overlaps the customer POS region by more than a predetermined percentage.

17. A method according to claim 16, wherein objects associated with transactions from a first return lane are analyzed using a first technique, and objects associated with transactions associated with a second return lane are analyzed using a second technique.

18. A method according to claim 17, wherein the first technique is the inclusion technique and the second technique is the mutual exclusion technique.

19. A method according to claim 16, wherein objects associated with transactions recorded from a first camera are analyzed using a first technique, and objects associated with transactions recorded from a second camera are analyzed using a second technique.

20. A method according to claim 19, wherein the first technique is the inclusion technique and the second technique is the mutual exclusions technique.