SURVEILLANCE SYSTEM AND METHODS

• US 20080201116A1
• Filed: 02/16/2007
• Published: 08/21/2008
• Est. Priority Date: 02/16/2007
• Status: Active Grant

• Current Assignee: I-Pro Company Limited
• Original Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Inventors: Lee, Kuo Chu, Yu, Juan, OZDEMIR, Hasan Timucin, Li, Hongbing, Kibey, Sameer, Joo, Namsoo, Liu, Lipin, Mosali, Supraja
• Granted Patent: US 7,667,596 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G08B 13/196 using television cameras
• CPC Class Codes: G08B 21/0423 detecting deviation from an expected pattern of behaviour or schedule
• CPC Class Codes: G08B 31/00 Predictive alarm systems characterised by extrapolation or other computation using updated historic data

Abstract

A surveillance system generally includes a data capture module that collects sensor data. A scoring engine module receives the sensor data and computes at least one of an abnormality score and a normalcy score based on the sensor data, at least one dynamically loaded learned data model, and a learned scoring method. A decision making module receives the at least one of the abnormality score and the normalcy score and generates an alert message based on the at least one of the abnormality score and the normalcy score and a learned decision making method to produce progressive behavior and threat detection.

Claims

1. A surveillance system, comprising:

a data capture module that collects sensor data;

a scoring engine module that receives the sensor data and computes at least one of an abnormality score and a normalcy score based on the sensor data, at least one dynamically loaded learned data model, and a learned scoring method; and

a decision making module that receives the at least one of the abnormality score and the normalcy score and generates an alert message based on the at least one of the abnormality score and the normalcy score and a learned decision making method to produce progressive behavior and threat detection.

2. The surveillance system of claim 1 further comprising a device configuration module that automatically loads the learned scoring methods, the learned decision making methods, and the learned model to at least one of the scoring engine module and the decision making module.

3. The surveillance system of claim 1 further comprising a model builder module that adaptively learns the model and wherein the scoring engine module computes the at least one of the abnormality score and the normalcy score based on the adaptively learned models.

4. The surveillance system of claim 1 further comprising a model builder module that builds the model based on at least one of a simulation of the sensor data and accumulated sensor data.

5. The surveillance system of claim 1 wherein the learned scoring method calculates an observed property of objects in motion against the model stored in a data cube to obtain a set of scores that represent at least one of similarity and difference scores between an object in motion and the learned model.

6. The surveillance system of claim 5 wherein the at least one of the similarity and difference scores are accumulated and normalized for the object in motion, to represent the at least on of normalcy and abnormality scores.

7. The surveillance system of claim 4 further comprising a graphical user interface that accepts parameters from a user to generate the simulation.

8. The surveillance system of claim 1 further comprising a learning module that adaptively learns at least one of the scoring methods, the decision making methods, and the learned model.

9. The surveillance system of claim 1 further comprising an alarm handling module that receives the alert message and generates an alarm message based on a further examination of the alert message.

10. The surveillance system of claim 1 wherein the data capture module collects sensor data from an image sensor and extracts object data from the sensor data, and wherein the scoring engine module computes the at least one of the abnormality score and the normalcy score based on the object data.

11. The surveillance system of claim 1 wherein the decision making module receives at least one of an abnormality score and a normalcy score generated from other sensor data and generates an alert message based on the at least one of the abnormality score and the normalcy score generated from the other sensor data.

12. A surveillance system, comprising:

a plurality of image sensing devices, wherein the image sensing devices each include;

a data capture module that collects sensor data;

a scoring engine module that receives the sensor data and computes at least one of an abnormality score and a normalcy score based on the sensor data, at least one dynamically loaded data model, and a learned scoring method; and

a decision making module that receives the at least one of the abnormality score and the normalcy score and generates an alert message based on the at least one of the abnormality score and the normalcy score and a learned decision making method to produce progressive behavior and threat detection.

13. The surveillance system of claim 12 wherein the decision making module of a first image sensing device receives the at least one of the abnormality score and the normalcy score from a second image sensing device, and wherein the decision making module of the first image sensing device generates the alert message based on the at least one of the abnormality score and the normalcy score from the second image sensing device.

14. The surveillance system of claim 12 further comprising a model builder module that adaptively learns the predetermined models.

15. The surveillance system of claim 12 wherein the image sensing devices each further include a device configuration module that automatically loads updated scoring methods, decision making methods, and the learned models to the image sensing device.

16. The surveillance system of claim 12 further comprising a model builder module that builds models based on a simulation of the sensor data and accumulated real sensor data.

17. The surveillance system of claim 16 further comprising a graphical user interface that accepts motion parameters from a user to generate the simulation.

18. The surveillance system of claim 12 further comprising a learning module that adaptively learns a decision making method and wherein the decision making method is selectively loaded to at least one of the plurality of image sensing devices.

19. The surveillance system of claim 12 further comprising an alarm handling module that receives the alert messages from the plurality of image sensing devices and generates an alarm message based on a further examination of the alert messages.

20. A surveillance method, comprising:

receiving sensor data;

dynamically loading data models;

computing at least one of an abnormality score and a normalcy score based on the sensor data and the dynamically loaded data models; and

generating an alert message based on the at least one of the abnormality score and the normalcy score.

21. The surveillance method of claim 20 further comprising selectively loading at least one of scoring methods and decision making methods to be used by at least one of the computing and the generating.

22. The surveillance method of claim 20 further comprising:

adaptively learning the data models, and

wherein the computing comprises computing the at least one of the abnormality score and the normalcy score based on the adaptively learned data models.

23. The surveillance method of claim 20 further comprising building the model based on a simulation of the sensor data.

24. The surveillance method of claim 20 further comprising:

adaptively learning a decision making method, and

wherein the generating comprises generating the alert message based on the adaptively learned decision making method.

25. The surveillance method of claim 20 further comprising:

further examining the alert message; and

generating an alarm message based on the further examining.

26. The surveillance method of claim 20 wherein the receiving comprises receiving sensor data from an image sensor.

27. The surveillance method of claim 26 further comprising:

extracting object data from the sensor data, and

wherein the computing further comprises computing the at least one of the abnormality score and the normalcy score based on the object data.