Network surveillance system

• US 9,185,355 B2
• Filed: 03/12/2013
• Issued: 11/10/2015
• Est. Priority Date: 11/20/2006
• Status: Active Grant

• Current Assignee: SenSen Networks Ltd.
• Original Assignee: SNAP Network Surveillance Pty., Limited (SenSen Networks Ltd.)
• Inventors: van den Hengel, Anton John, Dick, Anthony Robert, Brooks, Michael John
• Primary Examiner(s): Fitzpatrick, Atiba O
• Application Number:
  US13/796,271
• Publication Number:
  US 20130242109A1
• Time in Patent Office: 973 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06V 10/62 relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking
• 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 13/19645 Multiple cameras, each having view on one of a plurality of scenes, e.g. multiple cameras for multi-room surveillance or for tracking an object by view hand-over
• CPC Class Codes: G08B 13/19656 Network used to communicate with a camera, e.g. WAN, LAN, Internet
• CPC Class Codes: H04N 7/18 Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast

Abstract

A method for estimating the activity topology of a set of sensed data windows is described. Each of the sensed data windows related to a corresponding sensed region. The method includes the steps of determining a subset of sensed data windows that are not connected; and excluding the subset of sensed data windows from the set of sensed data windows. In one embodiment, the sensed data windows corresponding to image windows such as would be provided by a visual surveillance system.

Claims

1. A method for estimating the activity topology of a set of sensed data windows in a networked system of sensors, each of the sensed data windows related to a corresponding sensed region associated with a given sensor, the method including the steps:

determining a subset of sensed data windows having corresponding sensed regions that are not connected by comparing sensed data windows pair wise from the set of sensed data windows; and

excluding the subset of sensed data windows that are not connected from the set of sensed data windows thereby eliminating these pairs of sensed data windows from the networked system of sensors and determining the active topology.

2. The method of claim 1, wherein the step of comparing sensed data windows pair wise includes determining whether a pair of sensed data windows does not have overlapping sensed regions.

3. The method of claim 2, wherein the step of determining whether the pair of sensed data windows does not have overlapping sensed regions includes determining a first occupancy measure for a first sensed data window and a second occupancy measure for a second sensed data window, the first and second sensed data windows comprising the pair of sensed data windows, and comparing the first and second occupancy measures.

4. The method of claim 2, wherein the step of determining whether a pair of sensed data windows does not have overlapping sensed regions includes taking into account the likelihood of a false indication that a pair of sensed data windows do overlap.

5. The method of claim 4, wherein the step of taking into account the likelihood of a false indication that a pair of sensed data windows do overlap is based on previous data associated with the pair of sensed data windows.

6. The method of claim 4, wherein the step of taking into account the likelihood of a false indication that a pair of sensed data windows do overlap includes taking into account an error rate of a sensor or sensors associated with the pair of sensed data windows.

7. The method of claim 5, wherein the step of taking into account the likelihood of a false indication that a pair of sensed data windows do overlap includes taking into account an error rate of a sensor or sensors associated with the pair of sensed data windows.

8. The method of claim 1, wherein the sensed data window is an image window.

9. The method of claim 8, wherein the image window is provided by a camera in a network surveillance system.

10. The method of claim 1, further including the step of determining a further subset of sensor data windows that are connected.

11. A network surveillance system including:

a network of sensors, each sensor providing one or more sensed data windows each corresponding to a sensed region and forming in total a set of sensed data windows; and

data processing means to determine a subset of sensed data windows having corresponding sensed regions that are not connected by comparing sensed data windows pair wise from the set of sensed data windows and exclude the subset of sensed data windows that are not connected from the set of sensed data windows thereby eliminating these pairs of sensed data windows from the network surveillance system and determining an active topology of a set of sensed data windows.

19. The network surveillance system of claim 11, wherein the network of sensors includes cameras, and wherein the sensed data window is an image window.

20. The network surveillance system of claim 11, wherein the data processor is operable to determine a further subset of sensor data windows that are connected.

12. A non-transitory program storage device readable by machine, tangibly embodying a program of instructions to perform method steps for estimating the activity topology of a set of sensed data windows in a networked system of sensors, each of the sensed data windows related to a corresponding sensed region associated with a given sensor, the method steps including:

determining a subset of sensed data windows having corresponding sensed regions that are not connected by comparing sensed data windows pair wise from the set of sensed data windows; and

excluding the subset of sensed data windows that are not connected from the set of sensed data windows thereby eliminating these pairs of sensed data windows from the networked system of sensors and determining the active topology.

13. The network surveillance system of claim 12, wherein comparing sensed data windows pair wise includes determining whether a pair of sensed data windows does not have overlapping sensed regions.

14. The network surveillance system of claim 13, wherein determining whether the pair of sensed data windows does not have overlapping sensed regions includes determining a first occupancy measure for a first sensed data window and a second occupancy measure for a second sensed data window, the first and second sensed data windows comprising the pair of sensed data windows, and comparing the first and second occupancy measures.

15. The network surveillance system of claim 13, wherein determining whether a pair of sensed data windows does not have overlapping sensed regions includes taking into account the likelihood of a false indication that a pair of sensed data windows do overlap.

16. The network surveillance system of claim 15, wherein taking into account the likelihood of a false indication that a pair of sensed data windows do overlap is based on previous data associated with the pair of sensed data windows.

17. The network surveillance system of claim 15, wherein the step of taking into account the likelihood of a false indication that a pair of sensed data windows do overlap includes taking into account an error rate of a sensor or sensors associated with the pair of sensed data windows.

18. The network surveillance system of claim 16, wherein the step of taking into account the likelihood of a false indication that a pair of sensed data windows do overlap includes taking into account an error rate of a sensor or sensors associated with the pair of sensed data windows.