Bayesian methodology for geospatial object/characteristic detection

Patent metadata

Field	Value
Publication number	US-11087181-B2
Application number	US-201716468196-A
Country	US
Kind code	B2
Filing date	May 24, 2017
Priority date	May 24, 2017
Publication date	Aug 10, 2021
Grant date	Aug 10, 2021

Abstract

A location of an object of interest ( 205 ) is determined using both observations and non-observations. Numerous images ( 341 - 345 ) are stored in a database in association with image capture information, including an image capture location ( 221 - 225 ). Image recognition is used to determine which of the images include the object of interest ( 205 ) and which of the images do not include the object of interest. For each of multiple candidate locations ( 455 ) within an area of the captured images, a likelihood value of the object of interest existing at the candidate location is calculated using the image capture information for images determined to include the object of interest and using the image capture information for images determined not to include the object of interest. The location of the object is determined using the likelihood values for the multiple candidate locations.

First claim

The invention claimed is:

1. A method of determining a location of an object of interest, the method comprising:
identifying, from a database of images, a set of plural images that relate to a region of interest, each of the plural images having associated therewith image capture information including at least an image capture location;
applying an image recognition tool to each image in the set of plural images;
determining, based on the applying of the image recognition tool, which of the images include the object of interest and which of the images do not include the object of interest;
for each of multiple candidate locations in the region of interest, calculating a likelihood value of the object of interest existing at the candidate location using the image capture information for images in the set of plural images determined to include the object of interest and using the image capture information for images in the set of plural images determined not to include the object of interest; and
determining the location of the object using the likelihood values for the multiple candidate locations.

2. The method of claim 1 , wherein the image capture information for at least some of the images includes an image capture location and an image capture orientation, and wherein calculating the likelihood value of the object of interest existing at a given location comprises using the image capture orientation.

3. The method of claim 1 , wherein determining which of the images include an object of interest and which of the images do not include the object of interest comprises determining a confidence factor that the images include or do not include the object of interest, and wherein calculating the likelihood value of the object of interest existing at the location comprises using the using the determined confidence factor.

4. The method of claim 1 , wherein calculating the likelihood value of the object of interest existing at the candidate location comprises:
applying a factor with a first sign for the images in the set of plural images determined to include the object of interest, and
applying a factor with a second sign for the images in the set of plural images determined not to include the object of interest, the second sign being opposite the first sign.

5. The method of claim 1 , wherein the multiple candidate locations in the region of interest comprise a grid of multiple locations.

6. The method of claim 1 , wherein the multiple candidate locations include locations contained in a field of view of each image of the set of plural images.

7. The method of claim 1 , wherein the image recognition tool is configured to detect discrete objects.

8. The method of claim 1 , wherein the image recognition tool is configured to detect objects having specific characteristics.

9. The method of claim 1 , wherein the image recognition tool is selected from a library of image recognition tools.

10. A system, comprising:
memory storing a plurality of images in association with location information; and
one or more processor in communication with the memory, the one or more processors programmed to:
identify, from the plurality of images, a set of images that relate to a region of interest;
determine, using image recognition, which of the images include the object of interest;
determine, using image recognition, which of the images do not include the object of interest;
for each of multiple candidate locations in the region of interest, calculate a likelihood value of the object of interest existing at the candidate location using the location information for images in the set of images determined to include the object of interest and using the location information for images in the set of images determined not to include the object of interest; and
determining the location of the object using the likelihood values for the multiple candidate locations.

11. The system of claim 10 , wherein the location information includes image capture information.

12. The system of claim 11 , wherein the image capture information for at least some of the images includes an image capture location and an image capture orientation, and wherein calculating the likelihood value of the object of interest existing at a given location comprises using the image capture orientation.

13. The system of claim 10 , wherein determining which of the images include an object of interest and which of the images do not include the object of interest comprises determining a confidence factor that the images include or do not include the object of interest, and wherein calculating the likelihood value of the object of interest existing at the location comprises using the using the determined confidence factor.

14. The system of claim 10 , wherein calculating the likelihood value of the object of interest existing at the candidate location comprises:
applying a factor with a first sign for the images in the set of plural images determined to include the object of interest, and
applying a factor with a second sign for the images in the set of plural images determined not to include the object of interest, the second sign being opposite the first sign.

15. The system of claim 10 , wherein the multiple candidate locations in the region of interest comprise a grid of multiple locations.

16. The system of claim 10 , wherein the multiple candidate locations include locations contained in a field of view of each image of the set of images.

17. The system of claim 10 , wherein an image recognition tool is configured to detect discrete objects.

18. The system of claim 10 , wherein an image recognition tool is configured to detect objects having specific characteristics.

19. The system of claim 10 , wherein the one or more processors are further configured to provide the determined location information for output to a display.

20. A computer-readable medium storing instructions executable by a processor for performing a method of determining a location of an object of interest, the method comprising:
identifying, from a database of images, a set of plural images that relate to a region of interest, each of the images having associated therewith image capture information including at least an image capture location;
applying an image recognition tool to each image in the set of images;
determining, based on the applying of the image recognition tool, which of the images include the object of interest and which of the images do not include the object of interest;
for each of multiple candidate locations in the region of interest, calculating a likelihood value of the object of interest existing at the candidate location using the image capture information for images in the set of plural images determined to include the object of interest and using the image capture information for images in the set of images determined not to include the object of interest; and
determining the location of the object using the likelihood values for the multiple candidate locations.

Assignees

• Google Llc

Inventors

• Greene Michael

Classifications

• G06V20/176 Primary

  Urban or other man-made structures · CPC title

• G06F18/24155

  Bayesian classification · CPC title

• G06F18/29

  Graphical models, e.g. Bayesian networks · CPC title

• G06F16/387

  using geographical or spatial information, e.g. location · CPC title

• G06T2207/20104

  Interactive definition of region of interest [ROI] · CPC title