Apparatus and method for surveillance system using sensor arrays

• US 20090195401A1
• Filed: 01/31/2008
• Published: 08/06/2009
• Est. Priority Date: 01/31/2008
• Status: Abandoned Application

• Current Assignee: General Dynamics United Kingdom Limited (General Dynamics Corporation)
• Original Assignee: General Dynamics United Kingdom Limited (General Dynamics Corporation)
• Inventors: Burke, Peter, French, Richard, Maroney, Andrew
• Application Number:
  US12/010,941
• Publication Number:
  US 20090195401A1
• Time in Patent Office: Days
• US Class Current: 340/686.6
• 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 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

Embodiments of the invention may include a sensor system and a method used to track the behaviors of targets in an area under surveillance. The invention may include a sensor array located in the area that is capable of sending messages to a user when behavior of a tracked target is determined to be anomalous. In making the determination of anomalous behavior, the sensor system and method may generate and continuously refine a pattern of life model that may examine, for example, the paths a target may take within the sensor array and the end points of the paths taken. The sensor system and method may also incorporate any user defined conditions for anomalous behavior.

Claims

1. A method of detecting behavior, comprising:

sensing a target at a series of nodes using a plurality of sensors as the target moves through the plurality of sensors;

determining whether any two successive nodes in the series of nodes are nearest neighbors to each other;

retrieving a transition weight for a movement between two successive nodes in the series of nodes if the two successive nodes are nearest neighbors; and

alerting a user based, at least in part, on a comparison of the transition weight for the movement between the two successive nodes and a transition threshold.

2. The method of claim 1, wherein the behavior detected comprises anomalous behavior.

3. The method of claim 1, wherein two nodes are defined as nearest neighbors when the target can move between the two nodes without being sensed by another node.

4. The method of claim 1, further comprising:

determining a path weight for a path taken by the target from a first node to a final node, the first node and the final node belonging to the series of nodes;

determining a most-traveled-path weight from the first node to the final node; and

alerting the user based, at least in part, on a comparison of the path weight of the target'"'"'s path and the most-traveled-path weight between the first node and the final node.

5. The method of claim 1, further comprising:

determining a path weight for a path taken by the target from a first node to a final node, the first node and the final node belonging to the series of nodes;

determining a shortest-path weight from the first node to the final node; and

alerting the user based, at least in part, on a comparison of the path weight of the target'"'"'s path and the shortest-path weight between the first node and the final node exceeds a path threshold.

6. The method of claim 1, further comprising:

retrieving path data for all paths originating from a first node to sense the target, the first node belonging to the series of nodes;

sensing the target at a second node, the second node defined as one of the series of nodes after the first node;

determining a transition weight from the path data corresponding to a movement between the second node and the next node most likely to sense the target; and

sensing the target at a third node, the third node defined as one of the series of nodes after the second node;

retrieving a transition weight between the second node and the third node; and

alerting the user based, at least in part, on a comparison of the transition weight between the second node and the third node and the transition weight between the second node and the next most likely node.

7. The method of claim 1, further comprising:

determining a total path weight corresponding to the sum of the transition weights between the nodes in the series of nodes; and

alerting the user if the total path weight is less than the number of transitions between nodes in the series of nodes multiplied by an end-to-end threshold.

8. The method of claim 1, further comprising:

calculating the transition weights between the nodes in the plurality of sensors by using a set of historical data and the series of nodes sensing the target.

9. The method of claim 8, wherein calculating the transition weights between the nodes is done as the target travels through the plurality of sensors.

10. The method of claim 1, wherein the plurality of sensors comprise at least one of the following types of sensors:

acoustic, electromagnetic spectrum, seismic, vibrational, magnetic, radar, lidar, infrared, ultrasonic, beam-breakers, x-ray, laser, microwave, video, or audio.

11. The method of claim 1, wherein alerting the user comprises sending an image of the target to the user.

12. The method of claim 1, further comprising:

classifying the target into a target class, the target class includes at least one of a human or a vehicle.

13. A system for detecting behavior, comprising:

a plurality of sensors having a plurality of nodes configured to sense a target moving through the plurality of sensors; and

a processing system configured to;

receive a first signal from a first node of the plurality of nodes to sense the target;

receive a second signal from a second node of the plurality of nodes to sense the target;

determine whether the second node is a nearest neighbor to the first node;

retrieve a transition weight for a movement between the first node and a second node if the second node is a nearest neighbor to the first node; and

alert a user if the transition weight for the movement is less than a threshold.

14. The system of claim 13, wherein the behavior detected comprises anomalous behavior.

15. The system of claim 13, wherein two nodes are defined as nearest neighbors when the target can move between the two nodes without being sensed by another node.

16. The system of claim 13, wherein the processing system is further configured to:

retrieve path data for all paths originating from the first node;

determine a next node most likely to sense the target from the path data;

retrieve a transition weight between the second node and the next most likely node;

receive a third signal from a third node of the plurality of nodes;

determine whether the third node is a nearest neighbor to the second node;

retrieve a transition weight between the second node and the third node if the third node is a nearest neighbor to the second node; and

alert a user if the transition weight between the second node and the third node is less than the transition weight between the second node and the next most likely node.

17. The system of claim 16, wherein the processing system is further configured to:

receive a final signal from a final node;

determining a path weight for a path taken by the target from the first node to the final node;

determining a most-traveled-path weight from the first node to the final node; and

alerting the user if the difference between the path weight of the target'"'"'s path and the most-traveled-path weight between the first node and the final node exceeds a path threshold.

18. The system of claim 17, wherein the processing system is further configured to:

determining a total path weight corresponding to the sum of all transition weights between the first node and the final node; and

alerting the user if the total path weight is less than the number of transitions between the first node and the final node multiplied by an end-to-end threshold.

19. The system of claim 18, wherein the processing system is further configured to alert the user if the target triggers a user-defined rule.

20. The system of claim 19, wherein the user-defined rule includes the target remaining stationary for a predetermined period of time.

21. The system of claim 19, wherein the user-defined rule includes sensing of the target by a predetermined node.

22. The system of claim 13, wherein the processing system is further configured to calculate the transition weights between the nodes in the plurality of sensors by using a set of historical data and the series of nodes sensing the target.

23. The system of claim 22, wherein the processing system is further configured to calculate the transition weights between the nodes as the target travels through the plurality of sensors.

24. The system of claim 13, wherein the plurality of sensors comprise at least one of the following types of sensors:

acoustic, electromagnetic spectrum, seismic, vibrational, magnetic, radar, lidar, infrared, ultrasonic, beam-breakers, x-ray, laser, microwave, video, or audio.

25. The system of claim 24, wherein at least one of the plurality of sensors includes a magnetometer and a passive infrared detector.

26. The system of claim 13, wherein the processing system is further configured to alert the user by sending an image of the target to the user.

27. The system of claim 13, wherein the processing system is further configured to classify the target into a target class, the target class includes at least one of a human or a vehicle.