COMPLEX EVENT RECOGNITION IN A SENSOR NETWORK

• US 20150279182A1
• Filed: 03/31/2015
• Published: 10/01/2015
• Est. Priority Date: 04/01/2014
• Status: Active Grant

• Current Assignee: Motorola Solutions, Inc.
• Original Assignee: ObjectVideo Incorporated (Alarm.com Holdings, Inc.)
• Inventors: Choe, Tae Eun, Deng, Hongli, Kanaujia, Atul
• Granted Patent: US 10,186,123 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 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 13/19671 Addition of non-video data, i.e. metadata, to video stream

Abstract

Systems, methods, and manufactures for a surveillance system are provided. The surveillance system includes sensors having at least one non-overlapping field of view. The surveillance system is operable to track a target in an environment using the sensors. The surveillance system is also operable to extract information from images of the target provided by the sensors. The surveillance system is further operable to determine probablistic confidences corresponding to the information extracted from images of the target. The confidences include at least one confidence corresponding to at least one primitive event. Additionally, the surveillance system is operable to determine grounded formulae by instantiating predefined rules using the confidences. Further, the surveillance system is operable to infer a complex event corresponding to the target using the grounded formulae. Moreover, the surveillance system is operable to provide an output describing the complex event.

Claims

1. A surveillance system comprising a computing device comprising a processor and computer-readable storage device storing program instructions that, when executed by the processor, cause the computing device to perform operations comprising:

tracking a target in an environment using sensors;

extracting information from images of the target provided by the sensors;

determining a plurality of confidences corresponding to the information extracted from images of the target, the plurality of confidences including at least one confidence corresponding to at least one primitive event;

determining grounded formulae by instantiating predefined rules using the plurality of confidences;

inferring a complex event corresponding to the target using the grounded formulae; and

providing an output describing the complex event.

2. The system of claim 1, wherein extracting the information comprises:

segmenting scenes captured by the sensors;

detecting the at least one primitive event;

classifying the target; and

extracting attributes of the target.

3. The system of claim 2, wherein the at least one primitive event includes disappearing from a scene and reappearing in the scene.

4. The system of claim 1, wherein:

the predefined rules comprise hard rules and soft rules; and

the soft rules are associated with weights representing uncertainty.

5. The system of claim 1, wherein the operations further comprise constructing a Markov logic network from the grounded formulae.

6. The system of claim 1, wherein operations further comprise controlling the computing device to fuse the trajectory of the target across more than one of the sensors using a Markov logic network.

7. The system of claim 1, wherein:

at least one of the sensors is an non-calibrated sensor; and

the sensors have at least one non-overlapping field of view.

8. A method for a surveillance system comprising:

tracking a target in an environment using sensors;

extracting information from images of the target provided by the sensors;

determining a plurality of confidences corresponding to the information extracted from images of the target, the plurality of confidences including at least one confidence corresponding to at least one primitive event;

determining grounded formulae by instantiating predefined rules using the plurality of confidences;

inferring a complex event corresponding to the target using the grounded formulae; and

providing an output describing the complex event.

9. The method of claim 8, wherein extracting the information comprises:

segmenting scenes captured by the sensors;

detecting the at least one primitive event;

classifying the target; and

extracting attributes of the target.

10. The method of claim 9, wherein the at least one primitive event includes disappearing from a scene and reappearing in the scene.

11. The method of claim 8, wherein:

the predefined rules comprise hard rules and soft rules; and

the soft rules are associated with weights representing uncertainty.

12. The method of claim 8, wherein the program instructions further control the computing device to construct a Markov logic network from the grounded formulae.

13. The method of claim 8, wherein the program instructions further control the computing device to fuse the trajectory of the target across more than one of the sensors.

14. The method of claim 13, wherein the program instruction perform the fusing using a Markov logic network.

15. A computer-readable storage device storing computer-executable program instructions that, we executed by a computer, cause the computer to perform operations comprising:

tracking a target in an environment using sensors;

extracting information from images of the target provided by the sensors;

determining a plurality of confidences corresponding to the information extracted from images of the target, the plurality of confidences including at least one confidence corresponding to at least one primitive event;

determining grounded formulae by instantiating predefined rules using the plurality of confidences;

inferring a complex event corresponding to the target using the grounded formulae; and

providing an output describing the complex event.

16. The computer-readable storage device of claim 15, wherein extracting the information comprises:

segmenting scenes captured by the sensors;

detecting the at least one primitive event;

classifying the target; and

extracting attributes of the target.

17. The computer-readable storage device of claim 16, wherein the at least one primitive event includes disappearing from a scene and reappearing in the scene.

18. The computer-readable storage device of claim 15, wherein:

The predefined rules comprise hard rules and soft rules; and

the soft rules are associated with weights representing uncertainty.

19. The computer-readable storage device of claim 15, wherein the operations further comprise controlling the computing device to construct a Markov logic network from the grounded formulae.

20. The computer-readable storage device of claim 15, wherein the operations further comprise controlling the computing device to fuse the trajectory of the target across more than one of the sensors.