Method for monitoring a moving object and system regarding same

• US 20030123703A1
• Filed: 12/27/2001
• Published: 07/03/2003
• Est. Priority Date: 06/29/2001
• Status: Abandoned Application

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Pavlidis, Ioannis, Morellas, Vassilios
• Application Number:
  US10/034,780
• Publication Number:
  US 20030123703A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06T 2207/30241 Trajectory
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/277 involving stochastic approaches, e.g. using Kalman filters
• CPC Class Codes: G06T 7/97 Determining parameters from multiple pictures depth or shape recovery from multiple images G06T7/55; stereo camera calibration G06T7/85
• CPC Class Codes: G06V 10/24 Aligning, centring, orientation detection or correction of the image
• CPC Class Codes: G06V 10/28 Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
• CPC Class Codes: G06V 10/62 relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G08B 13/19608 Tracking movement of a target, e.g. by detecting an object predefined as a target, using target direction and or velocity to predict its new position
• CPC Class Codes: G08B 13/19643 wherein the cameras play different roles, e.g. different resolution, different camera type, master-slave camera
• CPC Class Codes: G08B 13/19667 Details realated to data compression, encryption or encoding, e.g. resolution modes for reducing data volume to lower transmission bandwidth or memory requirements
• CPC Class Codes: G08G 1/04 using optical or ultrasonic detectors

Abstract

A method and system for use in monitoring a search area includes the provision of a plurality of time varying distributions for each pixel of image data representative of a search area. The plurality of time varying distributions for each pixel are ordered based on a probability of the time varying distribution being representative of background or foreground information in the search area. For each pixel, an attempt to match update pixel value data to each of all of the plurality of time varying distributions provided for the corresponding pixel is performed to determine if the update pixel value data is representative of background or foreground information in the search area.

Claims

1. A method for use in monitoring a search area, the method comprising:

providing frames of image data representative of a search area, the image data comprising pixel value data for a plurality of pixels;

providing a plurality of time varying distributions for each pixel based on the pixel value data;

providing at least one frame of update image data representative of the search area in an update cycle, the frame of image data comprising update pixel value data for each of the plurality of pixels; and

attempting to match the update pixel value data for each pixel to each of all of the plurality of time varying distributions provided for the pixel;

updating the plurality of time varying distributions for each pixel based on whether the update pixel value data matches one of the plurality of time varying distributions provided for the pixel; and

ordering the updated plurality of time varying distributions for each pixel based on a probability of the time varying distributions thereof being representative of background or foreground information in the search area for use in determining whether the pixel is to be considered background or foreground information.

2. The method of claim 1, wherein attempting to match the update pixel value data for each pixel to each of all of the plurality of time varying distributions provided for the pixel comprises:

providing a narrow distribution for the pixel; and

comparing the narrow distribution to each of all of the plurality of time varying distributions provided for the pixel.

3. The method of claim 2, wherein comparing the narrow distribution to each of all of the plurality of time varying distributions provided for the pixel comprises computing divergence between the narrow distribution created for the pixel and each of all the plurality of time varying distributions provided for the pixel.

4. The method of claim 2, wherein updating the plurality of time varying distributions for each pixel comprises generating a pooled distribution based on the narrow distribution and a matched distribution if the narrow distribution matches one of the plurality of time varying distributions, and further wherein ordering the updated plurality of time varying distributions comprises determining if the pixel is representative of background or foreground information in the search area based on a position of the pooled distribution within the order of the updated plurality of time varying distributions.

5. The method of claim 2, wherein updating the plurality of time varying distributions for each pixel comprises replacing one of the plurality of time varying distributions with a new distribution if the narrow distribution does not match one of the plurality of time varying distributions, and further wherein ordering the updated plurality of time varying distributions comprises assuring that the new distribution is representative of foreground information in the search area.

6. The method of claim 1, wherein ordering the updated plurality of time varying distributions for each pixel is based on weights associated with the plurality of time varying distributions.

7. The method of claim 1, wherein at least a portion of the foreground information corresponds to one or more moving objects, and further wherein the method comprises tracking the one or more moving objects in the search area to determine object paths for the one or more moving objects.

8. The method of claim 7, wherein tracking the one or more moving objects in the search area comprises:

calculating blobs based on pixels representative of foreground information; and

filtering out blobs having less than a predetermined pixel area size.

9. The method of claim 8, wherein the method further comprises grouping the blobs into object paths representative of one or more moving objects.

10. The method of claim 9, wherein grouping the blobs into object paths comprises grouping the blobs into object paths using a multiple hypotheses tracking algorithm.

11. The method of claim 7, wherein the method further comprises:

providing one or more defined normal and/or abnormal object path feature models based on one or more characteristics associated with normal or abnormal events; and

comparing the one or more object paths to the one or more defined normal and/or abnormal object path feature models to determine whether the one or more object paths are normal or abnormal.

12. The method of claim 11, wherein providing one or more defined normal and/or abnormal object path feature models comprises providing one or more defined threatening and/or non-threatening object path feature models based on one or more characteristics associated with threatening events;

and

wherein comparing the one or more object paths to the one or more defined normal and/or abnormal object path feature models comprises comparing at least the one or more object path, or data associated therewith, to the one or more defined threatening and/or non-threatening object path feature models to determine whether the one or more object paths appear to indicate that a threatening event is occurring.

13. The method of claim 1, wherein the method further comprises positioning a plurality of imaging devices to cover an entire defined search area, wherein each field of view of each imaging device comprises a field of view portion which overlaps with at least one other field of view of another imaging device, wherein the field of view portion which overlaps is greater than about 25 percent device and less than about 85 percent of the field of view of the imaging device.

14. A system for use in monitoring a search area, the system comprising:

one or more imaging devices operable to provide frames of image data representative of the search area, the image data comprising pixel value data for a plurality of pixels, wherein the frames of image data comprise at least one frame of update image data representative of the search area in an update cycle, the frame of update image data comprising update pixel value data for each of the plurality of pixels; and

a computer apparatus operable to;

attempt to match the update pixel value data for each pixel to each of all of the plurality of time varying distributions provided for the pixel;

update the plurality of time varying distributions for each pixel based on whether the update pixel value data matches one of the plurality of time varying distributions provided for the pixel; and

order the updated plurality of time varying distributions for each pixel based on a probability of the time varying distributions thereof being representative of background or foreground information in the search area for use in determining whether the pixel is to be considered background or foreground information.

15. The system of claim 14, wherein the computer apparatus is further operable, with respect to each pixel, to:

provide a narrow distribution for the pixel; and

compare the narrow distribution to each of all of the plurality of time varying distributions provided for the pixel.

16. The system of claim 15, wherein the computer apparatus is further operable, with respect to each pixel, to compute divergence between the narrow distribution provided for the pixel and each of all the plurality of time varying distributions provided for the pixel.

17. The system of claim 15, wherein the computer apparatus is further operable, with respect to each pixel, to:

update the plurality of time varying distributions by generating a pooled distribution based on the narrow distribution and a matched distribution if the narrow distribution matches one of the plurality of time varying distributions; and

determine if the pixel is representative of background or foreground information in the search area based on position of the pooled distribution within the order of the updated plurality of time varying distributions.

18. The system of claim 15, wherein the computer apparatus is further operable, with respect to each pixel, to:

update the plurality of time varying distributions by replacing one of the plurality of time varying distributions with a new distribution if the narrow distribution does not match one of the plurality of time varying distributions; and

assure that the new distribution is representative of foreground information in the search area.

19. The system of claim 14, wherein the computer apparatus is further operable to order the updated plurality of time varying distributions for each pixel based on weights associated with the plurality of time varying distributions.

20. The system of claim 14, wherein at least a portion of the foreground information corresponds to one or more moving objects, and further wherein the computer apparatus is operable to track the one or more moving objects in the search area to determine object paths for the one or more moving objects.

21. The system of claim 20, wherein the computer apparatus is further operable to:

calculate blobs based on pixels representative of foreground information; and

filter out blobs having less than a predetermined pixel area size.

22. The system of claim 21, wherein the computer apparatus is further operable to group the blobs into object paths representative of one or more moving objects.

23. The system of claim 22, wherein the computer apparatus is further operable to group the blobs into object paths using a multiple hypotheses tracking algorithm.

24. The system of claim 20, wherein the computer apparatus is further operable to:

provide one or more defined normal and/or abnormal object path feature models based on one or more characteristics associated with normal or abnormal events; and

compare the one or more object paths to the one or more defined s normal and/or abnormal object path feature models to determine whether the one or more object paths are normal or abnormal.

25. The system of claim 20, wherein the computer apparatus is further operable to:

provide one or more defined threatening and/or non-threatening object path feature models based on one or more characteristics associated with threatening events; and

compare at least the one or more object path, or data associated therewith, to the one or more defined threatening and/or non-threatening object path feature models to determine whether the one or more object paths appear to indicate that a threatening event is occurring.

26. The system of claim 14, wherein the one or more imaging devices comprise a plurality of imaging devices positioned to cover an entire defined search area, wherein each field of view of each imaging device comprises a field of view portion which overlaps with at least one other field of view of another imaging device, wherein the field of view portion which overlaps is greater than about 25 percent and less than about 85 percent of the field of view of the imaging device.