Method for Automatically Characterizing the Behavior of One or More Objects

• US 20090210367A1
• Filed: 03/28/2006
• Published: 08/20/2009
• Est. Priority Date: 03/28/2006
• Status: Abandoned Application

• Current Assignee: Edinburgh Research and Innovation Limited
• Original Assignee: Edinburgh Research and Innovation Limited
• Inventors: Armstrong, James Douglas, Heward, James Alexander, Baker, Dean Adam
• Application Number:
  US12/225,625
• Publication Number:
  US 20090210367A1
• Time in Patent Office: Days
• US Class Current: 706/13
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 7/277 involving stochastic approaches, e.g. using Kalman filters
• CPC Class Codes: G06V 10/24 Aligning, centring, orientation detection or correction of the image
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16

Abstract

Automatically characterizing a behavior of an object or objects by processing object data to obtain a data set that records a measured parameter set for each object over lime, providing a learning input that identifies when the measured parameter set of an object is associated with a behavior, processing the data set in combination with the learning input to determine which parameters of the parameter set over which range of respective values characterize the behavior; and sending information that identifies which parameters of the parameter set over which range of respective values characterize the behavior for use in a process that uses the characteristic parameters and their characteristic ranges to process second object data and automatically identify when the behavior occurs. Also disclosed is a method of tracking one or more objects.

Claims

1. A method for automatically characterizing a behavior of an object or objects, comprising:

processing object data to obtain a data set that records a measured parameter set for each object over time;

providing a learning input that identifies when the measured parameter set of an object is associated with a behavior;

processing the data set in combination with the learning input to determine which parameters of the parameter set over which range of respective values characterize the behavior; and

sending information that identifies which parameters of the parameter set over which range of respective values characterize the behavior for use in a process that uses the characteristic parameters and their characteristic ranges to process second object data and automatically identify when the behavior occurs.

2. A method as claimed in claim 1, wherein the independent moving objects are animals capable of independent relative movement.

3. A method as claimed in claim 2, wherein the animals are one of the animals selected from the group comprising:

rodents;

flies, zebra fish and humans.

7. A method as claimed in claim 1, wherein processing the data set in combination with the learning input to determine which parameters of the parameter set over which range of respective values characterize the behavior involves the use of a learning mechanism.

8. A method as claimed in claim 1, wherein processing the data set in combination with the learning input to determine which parameters of the parameter set over which range of respective values characterize the behavior involves the use of a genetic algorithm.

9. A method as claimed in claim 8, wherein chromosomes used in the genetic algorithm comprises a gene for each of the parameters in the measured parameter set that may be switched on or off.

10. A method as claimed in claim 8, wherein chromosomes used in the genetic algorithm comprises a gene that specifies the number of clusters of parameters necessary for characterizing the behavior.

11. A method as claimed in claim 8, wherein chromosomes used in the genetic algorithm comprises a gene that specifies a time period over which the fitness of a chromosome is assessed.

12. A method as claimed in claim 8, wherein a chromosome from a population of chromosomes used by the genetic algorithm, defines a parameter space and how many clusters the parameter space is divided into, and the extent to which the sub-set of the data set that lies within the clusters correlates with the sub-set of the data set associated with a behavior determines the fitness of the chromosome.

13. A method as claimed in claim 1 wherein the object data is video.

14. A method as claimed in claim 1, wherein the characterized behavior is an interaction of moving objects.

15. A record medium tangibly embodying computer program instructions which when loaded into a processor, enable the processor to perform steps in the method of claim 1.

4. (canceled)

5. (canceled)

6. (canceled)

16. A system for automatically characterizing a behavior of an object or objects, comprising:

means for providing a learning input that identifies when a measured parameter set of an object is associated with a behavior;

means for processing a data set, which records the measured parameter set for each object over time, in combination with the learning input to determine which parameters of the parameter set over which range of respective values characterize the behavior; and

means for outputting information that identifies which parameters of the parameter set over which range of respective values characterize the behavior.

17. A method of tracking one or more objects comprising:

processing first data to identify discrete first groups of contiguous data values that satisfy a first criterion or first criteria;

processing second subsequent data to identify discrete second groups of contiguous data values that satisfy a second criterion or second criteria;

performing mappings between the first groups and the second groups;

using the mappings to determine whether a second group represents a single object or a plurality of objects;

measuring one or more parameters of a second group when it is determined that the second group represents a single object;

processing a second group, when it is determined that the second group represents N (N>

1) objects, to resolve the second group into N subgroups of contiguous data values that satisfy the second criterion or second criteria;

measuring the one or more parameters of the sub groups; and

mapping the plurality of subgroups to the plurality of objects.

18. A method as claimed in claim 17, wherein mapping the plurality of subgroups to respective objects is based upon matching a measured parameter.

21. A method as claimed in claim 17, wherein mapping the plurality of subgroups to respective objects is based upon a history of a measured parameter.

22. A method as claimed in claim 17, wherein mapping the plurality of subgroups to respective objects is according to a first method if a third criterion or criteria are satisfied and wherein mapping the plurality of subgroups to respective objects is according to a second method if a fourth criterion or criteria are satisfied.

25. A method as claimed in claim 17, wherein the first data is a first video frame, the second data is a second subsequent video frame, and the data values are pixel values and wherein mapping the plurality of subgroups to respective objects is based upon a measure of the distance of a subgroup in the second frame from objects in the first frame, so long as the shortest one of the distances exceeds a threshold value and is otherwise based upon a measure of the motion of a moving object derived from previous frames including the first frame and the distance of a subgroup in the second frame from a predicted position of the moving object, the method further comprising:

processing a video frame to identify discrete groups of contiguous pixel values that satisfy a criterion or criteria involves dividing the first video frame of size (h×

w) into a plurality of windows and performing mean thresholding individually to each window.

28. A method as claimed in claim 17, wherein performing mappings between the first groups and the second groups involves the identification of a ‘

no match’

event in which one of the first groups appears to have entered (or left) and one of the second groups appears to have simultaneously left (or entered) and forcing the mapping of the one of the first groups that appears to have entered (or left) with the one of the second groups that appears to have simultaneously left (or entered) and further comprising resolving conflicts in mappings between the first groups and the second groups in dependence upon whether it appears that one or more of the second groups has entered or left compared to the first frame.

19. (canceled)

20. (canceled)

23. (canceled)

24. (canceled)

26. (canceled)

27. (canceled)

29-33. -33. (canceled)

34. A system for tracking one or more objects comprising:

a processor operable to process first data to identify discrete first groups of contiguous data values that satisfy a first criterion or first criteria;

process second subsequent data to identify discrete second groups of contiguous data values that satisfy a second criterion or second criteria;

perform mappings between the first groups and the second groups;

use the mappings to determine whether a second group represents a single object or a plurality of objects;

measure one or more parameters of a second group when it is determined that the second group represents a single object;

process a second group, when it is determined that the second group represents N (N>

1) objects, to resolve the second group into N subgroups of contiguous data values that satisfy the second criterion or second criteria;

measure the one or more parameters of the sub groups; and

map the plurality of subgroups to the plurality of objects.