Information Processing System for Classifying and/or Tracking an Object

• US 20080235318A1
• Filed: 02/07/2008
• Published: 09/25/2008
• Est. Priority Date: 02/09/2007
• Status: Active Grant

• Current Assignee: Raytheon Company (Rtx Corporation)
• Original Assignee: Raytheon Company (Rtx Corporation)
• Inventors: Khosla, Deepak, Guillochon, James, Choe, Howard C.
• Granted Patent: US 8,010,658 B2
• Time in Patent Office: Days
• US Class Current: 709/201
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06T 7/277 involving stochastic approaches, e.g. using Kalman filters
• CPC Class Codes: G06V 10/255 Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 10/62 relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking

Abstract

According to one embodiment, a computing system includes a computing node coupled to a number of sensors. The sensors are operable to generate records from received information and transmit these records to the computing node. The computing node is operable to bind the plurality of records in a plurality of classifications using a multiple level classifier such that each classification has a differing level of specificity.

Claims

1. A distributed computer system comprising:

a plurality of sensors operable to receive information about an object and generate records from the received information;

a first computing node coupled to the plurality of sensors and operable to;

receive a first plurality of records from the plurality of sensors; and

bind the first plurality of records in a plurality of classifications using a Bayesian reasoning classifier, each of the plurality of classifications having a differing level of specificity;

a second computing node coupled to the plurality of sensors and the first computing node, the second computing node operable to;

receive a second plurality of records at a specified period of time after the first plurality of records are received;

bind the second plurality of records and the first plurality of records in the plurality of classifications; and

determine, using an information form of a Kalman filter, an estimated trajectory of the object according to the first plurality of records and the second plurality of records.

2. Code embodied in a computer-readable medium, when executed by a computer operable to perform at least the following:

receive, using a first computing node, a plurality of records from a plurality of sensors coupled to the first computing node through a network, the plurality of sensors operable to generate a plurality of records from information about an object; and

bind the plurality of records in a plurality of classifications using a multiple level classifier, each of the plurality of classifications having a differing level of specificity.

3. The code of claim 2, further operable to bind a first portion of the plurality of records using a second computing node, the second computing node communicating with the first computing node through a network, receive, using the first computing node, the first portion from a second computing node, and bind the first portion to a second portion of the plurality of records.

4. The code of claim 2, further operable to receive a second plurality of records at a specified period of time after the first plurality of records are received, and determine an estimated trajectory of the object according to the first plurality of records and the second plurality of records.

5. The code of claim 4, further operable to associate the first plurality of records with the second plurality of records using a scoring algorithm.

6. The code of claim 4, further operable to determine the estimated trajectory of the object using an information form of the Kalman filter.

7. The code of claim 2, further operable to bind the plurality of records in the plurality of classifications using a Bayesian reasoning classifier.

8. The code of claim 2, further operable to bind the plurality of records in the plurality of classifications by recursively constructing confusion matrices.

9. A computer system comprising:

a plurality of sensors operable to receive information about an object, the plurality of sensors operable to generate a plurality of records from the received information;

a first computing node coupled to the plurality of sensors through a network and operable to;

receive the plurality of records from the plurality of sensors; and

bind the plurality of records in a plurality of classifications using a multiple level classifier, each of the plurality of classifications having a differing level of specificity.

10. The computer system of claim 9, further comprising a second computing node coupled to the first computing node through a network, the second computing node operable to bind a second plurality of records, the first computing node operable to receive the second plurality of records from the second computing node, and bind the second plurality of records to the first plurality of records.

11. The computer system of claim 9, wherein the computing node is further operable to receive a second plurality of records at a specified period of time after the first plurality of records are received, and determine an estimated trajectory of the object according to the first plurality of records and the second plurality of records.

12. The computer system of claim 11, wherein the computing node is further operable to associate the first plurality of records with the second plurality of records using a scoring algorithm.

13. The computer system of claim 11, wherein the computing node is further operable to determine the estimated trajectory of the object using an information form of the Kalman filter.

14. The computer system of claim 9, wherein the computing node is further operable to bind the plurality of records in the plurality of classifications using a Bayesian reasoning classifier.

15. The computer system of claim 9, wherein the computing node is further operable to bind the plurality of records in the plurality of classifications by recursively combining confusion matrices.

16. A method comprising:

receiving, using a first computing node, a plurality of records from a plurality of sensors coupled to the first computing node through a network, the plurality of sensors operable to generate a plurality of records from information about an object; and

binding the plurality of records in a plurality of classifications using a multiple level classifier, each of the plurality of classifications having a differing level of specificity.

17. The method of claim 16, further comprising binding a second plurality of records using a second computing node, the second computing node communicating with the first computing node through a network, receiving, using the first computing node, the second plurality of records from the second computing node, and binding the second plurality of records to the first plurality of records.

18. The method of claim 16, further comprising receiving a second plurality of records at a specified period of time after the first plurality of records are received, and determining an estimated trajectory of the object according to the first plurality of records and the second plurality of records.

19. The method of claim 18, further comprising associating the first plurality of records with the second plurality of records using a scoring algorithm.

20. The method of claim 18, further comprising determining the estimated trajectory of the object using an information form of the Kalman filter.

21. The method of claim 16, further comprising binding the plurality of records in the plurality of classifications using a Bayesian reasoning classifier.