Method and apparatus to disambiguate state information for multiple items tracking

• US 20080154555A1
• Filed: 10/13/2006
• Published: 06/26/2008
• Est. Priority Date: 10/13/2006
• Status: Abandoned Application

• Current Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Schonfeld, Dan, Mohamed, Magdi A., Qu, Wei
• Application Number:
  US11/549,542
• Publication Number:
  US 20080154555A1
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06V 10/24 Aligning, centring, orientation detection or correction of the image
• CPC Class Codes: G06V 10/62 relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking

Abstract

Automatic use (102) of a disjoint probabilistic analysis of captured temporally parsed data (101) regarding at least a first and a second item serves to facilitate disambiguating state information as pertains to the first item from information as pertains to the second item. This can also comprise, for example, using a joint probability as pertains to the temporally parsed data for the first item and the temporally parsed data for the second item, by using, for example, a Bayesian-based probabilistic analysis of the temporally parsed data.

Claims

1. A method comprising:

capturing temporally parsed data regarding at least a first and a second item;

automatically using, at least in part, disjoint probabilistic analysis of the temporally parsed data to disambiguate state information as pertains to the first item from information as pertains to the second item.

2. The method of claim 1 wherein automatically using, at least in part, probabilistic analysis of the temporally parsed data to disambiguate state information as pertains to the first item from information as pertains to the second item comprises using a joint probability as pertains to the temporally parsed data for the first item and the temporally parsed data for the second item.

3. The method of claim 2 wherein automatically using, at least in part, probabilistic analysis of the temporally parsed data comprises using, at least in part, a Bayesian-based probabilistic analysis of the temporally parsed data.

4. The method of claim 3 wherein using, at least in part, a Bayesian-based probabilistic analysis of the temporally parsed data comprises using:

a transitional probability as pertains to temporally parsed data for the first item as was captured at a first time and temporally parsed data for the first item as was captured at a second time that is different than the first time;

a transitional probability as pertains to temporally parsed data for the second item as was captured at the first time and temporally parsed data for the second item as was captured at the second time.

5. The method of claim 4 wherein:

using a transitional probability as pertains to temporally parsed data for the first item as was captured at a first time and temporally parsed data for the first item as was captured at a second time further comprises using a transitional probability as pertains to first state information for the first item as pertains to the first time and second state information for the first item as pertains to the second time;

using a transitional probability as pertains to temporally parsed data for the second item as was captured at the first time and temporally parsed data for the second item as was captured at the second time further comprises using a transitional probability as pertains to first state information for the second item as pertains to the first time and second state information for the second item as pertains to the second time.

6. The method of claim 5 wherein using, at least in part, a Bayesian-based probabilistic analysis of the temporally parsed data further comprises using:

a conditional probability as pertains to temporally parsed data for the first item and state information for the first item;

a conditional probability as pertains to temporally parsed data for the second item and state information for the second item.

7. The method of claim 1 wherein the first and second item each comprise an object.

8. The method of claim 1 wherein the first and second item each comprise a discernable energy wave.

9. The method of claim 1 wherein automatically using, at least in part, disjoint probabilistic analysis of the temporally parsed data to disambiguate state information as pertains to the first item from information as pertains to the second item comprises automatically using, at least in part, disjoint probabilistic analysis of the temporally parsed data to disambiguate state information as pertains to the first item from state information as pertains to the second item.

10. The method of claim 1 wherein capturing temporally parsed data regarding at least a first and a second item comprises capturing temporally parsed data regarding at least a first and a second item using only a single data capture device.

11. An apparatus comprising:

a memory having captured temporally parsed data regarding at least a first and a second item stored therein;

a processor operably coupled to the memory and being configured and arranged to automatically use, at least in part, disjoint probabilistic analysis of the temporally parsed data to disambiguate state information as pertains to the first item from information as pertains to the second item.

12. The apparatus of claim 11 wherein the processor is further configured and arranged to automatically use a joint probability as pertains to the temporally parsed data for the first item and the temporally parsed data for the second item.

13. The apparatus of claim 12 wherein the processor is further configured and arranged to automatically use, at least in part, a Bayesian-based probabilistic analysis of the temporally parsed data.

14. The apparatus of claim 13 wherein the Bayesian-based probabilistic analysis of the temporally parsed data comprises using:

a transitional probability as pertains to temporally parsed data for the first item as was captured at a first time and temporally parsed data for the first item as was captured at a second time that is different than the first time;

a transitional probability as pertains to temporally parsed data for the second item as was captured at the first time and temporally parsed data for the second item as was captured at the second time.

15. The apparatus of claim 14 wherein the processor is further configured and arranged to:

use a transitional probability as pertains to first state information for the first item as pertains to the first time and second state information for the first item as pertains to the second time;

use a transitional probability as pertains to first state information for the second item as pertains to the first time and second state information for the second item as pertains to the second time.

16. The apparatus of claim 15 wherein the processor is further configured and arranged, at least in part, to use the Bayesian-based probabilistic analysis of the temporally parsed data by using:

a conditional probability as pertains to temporally parsed data for the first item and state information for the first item;

a conditional probability as pertains to temporally parsed data for the second item and state information for the second item.

17. The apparatus of claim 11 wherein the first and second item each comprise an object.

18. The apparatus of claim 11 wherein the first and second item each comprise a discernable energy wave.

19. The apparatus of claim 11 wherein the processor is configured and arranged to automatically use, at least in part, disjoint probabilistic analysis of the temporally parsed data to disambiguate state information as pertains to the first item from information as pertains to the second item by automatically using, at least in part, disjoint probabilistic analysis of the temporally parsed data to disambiguate state information as pertains to the first item from state information as pertains to the second item.

20. The apparatus of claim 11 further comprising:

a single image capture device operably coupled to the memory such that the captured temporally parsed data is captured via the single image capture device.