EVENT DETECTION SYSTEM USING ELECTRONIC TRACKING DEVICES AND VIDEO DEVICES

• US 20100308993A1
• Filed: 08/04/2010
• Published: 12/09/2010
• Est. Priority Date: 06/27/2007
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Ma, Yunqian, Anderson, Bruce W., Whillock, Rand P.
• Granted Patent: US 8,648,718 B2
• Time in Patent Office: Days
• US Class Current: 340/539.25
• CPC Class Codes: G07C 9/28 the pass enabling tracking or indicating presence

Abstract

An event detection system includes a processor, an electronic tracking device, and one or more transmitters. Each of the one or more transmitters can be configured to be associated with a particular individual of a group of individuals. The processor can be configured to cluster data from the one or more transmitters, and the processor can be configured to analyze the clustered data to determine a group behavior pattern among the group of individuals. In an embodiment, video data can be combined with the electronic tracking device data in the event detection system.

Claims

1. A system comprising:

a processor;

a radio frequency-based electronic tracking device, the electronic tracking device coupled to the processor; and

one or more transmitters, the electronic tracking device configurable to read the one or more transmitters;

wherein each of the one or more transmitters is configurable to be associated with a particular individual of a group of individuals;

wherein the processor is configurable to cluster data from the one or more transmitters; and

wherein the processor is configurable to analyze the clustered data to determine a group behavior pattern among the group of individuals.

2. The system of claim 1, comprising one or more video sensing devices, the one or more video sensing devices coupled to the processor, wherein the processor is configurable to associate data from the one or more transmitters and data from the one or more video sensing devices.

3. The system of claim 2, wherein the processor is configurable to identify anomalies between the data from the one or more transmitters and the data from the one or more video sensing devices.

4. The system of claim 2, wherein the processor is configurable to first receive data from the one or more video sensors, then to receive data from the one or more transmitters, and then to use the data from the one or more transmitters to identify a person in the data from the one or more video sensors.

5. The system of claim 2, wherein the association between the data from the one or more transmitters and the data from the one or more video sensing devices comprises a dynamic Bayesian network.

6. The system of claim 5, wherein the dynamic Bayesian network comprises a complex event level, a first simple event level, and second simple event level, wherein data in the first simple event level and the second simple event level originate from both the electronic tracking device and the video sensor.

7. The system of claim 6, wherein the video sensor and the electronic tracking device are configurable to process data to generate a simple activity.

8. The system of claim 2, comprising a database, coupled to the processor, for storing the data from the one or more transmitters and the data from the one or more video sensors.

9. The system of claim 2, wherein the processor is configurable to use the data from the one or more transmitters and the data from the one or more video sensing devices for one or more of event monitoring, social behavior monitoring, and system self-learning.

10. The system of claim 2, wherein the group behaviors include at least one of an identification of an illegal activity and an altercation among two or more people.

11. The system of claim 1, wherein the data from the one or more transmitters is clustered as a function of one or more transmitter identifiers, transmitter locations, and transmitter timestamps.

12. The system of claim 1, wherein the group behavior patterns include at least one of an identification of the members of a group, an identification of a group leader, a change in an established pattern or activity of a group, a tracking of an object from a first individual to a second individual, and an entry into a restricted area by an unauthorized individual.

13. The system of claim 1, wherein the processor is configurable in an unsupervised learning mode to detect patterns in real-time operation.

14. The system of claim 1, wherein the electronic tracking device includes one or more of a radio frequency identification device, an ultra-wide band device, a biometrics identification device, and a card-based identification device.

15. A system comprising:

a processor;

a radio frequency-based electronic tracking device coupled to the processor; and

one or more transmitters, the electronic tracking device configurable to read the one or more transmitters;

wherein each of the one or more transmitters is configurable to be associated with a particular object among a group of objects;

wherein the processor is configurable to cluster data from the one or more transmitters; and

wherein the processor is configurable to analyze the clustered data to determine a group behavior pattern among a group of people who come into contact with one or more objects from the group of objects.

16. The system of claim 15, wherein the electronic tracking device and the one or more transmitters comprise one or more of a radio frequency identification (RFID) device, an ultra wide band tracking device, a biometrics identification device, and a card-based identification device.

17. The system of claim 16, comprising a video sensing device, the video sensing device coupled to the processor, wherein the processor is configurable to process and associate the data from the video sensing device and the clustered data from the one or more transmitters.

18. A process comprising:

reading data from a plurality of electronic tracking transmitters, each electronic tracking transmitter associated with a particular individual in a group of individuals;

clustering the electronic tracking transmitter data; and

analyzing the clustered electronic tracking transmitter data to determine a group behavior pattern associated with the group of individuals.

19. The process of claim 18, comprising:

collecting video data; and

associating the video data with the electronic tracking transmitter data.

20. The process of claim 19, comprising associating the video data and the electronic tracking transmitter data using a dynamic Bayesian network.