BAYESIAN METHODOLOGY FOR GEOSPATIAL OBJECT/CHARACTERISTIC DETECTION
FIG6 is a top-view illustration of another example of observations and non-observations according to aspects of the disclosure
FIG6 illustrates another example where directional information for the imagery is not used, though images captured from various angles are used. In this example, a first roadway 610 intersects with a second roadway 615. Images are captured from locations 621, 622 on the second roadway 615, and from locations 623-625 on the second roadway. Accordingly, while the fields of view 631-635 are radial, the positions of the capture locations 621-625 along different axes may further increase the accuracy of localization of object 605. For example, as shown, the object 605 is within the fields of view 632 and 633, but not fields of view 631, 634, 635. The confidence score computed for candidate locations within both fields of view 632, 633 should be greater than those computed for other locations. Accordingly, the normalized value for such candidate locations within both fields of view 632, 633, which factors in non-observations from adjacent fields of view 631, 634, 635 should indicate a high likelihood of the object's presence.
view
605
object
633
view
632
view
621
610
first roadway
615
second roadway
631
view
['1. A method (1100) of determining a location of an object of interest (205, 305), the method comprising: identifying (1110), from a database of images, a set of plural images (341-345) that relate to a geographical region of interest, each of the plural images having associated therewith image capture information including at least an image capture location (221-225) and an image capture orientation; applying an image recognition tool to each image in the set of plural images; determining (1120, 1130), 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 geographical region of interest, each candidate location corresponding to a sub-division (455) of the geographical region of interest and having a prior likelihood that the object of interest is located at the candidate location, calculating (1140) a likelihood value representing a posterior probability 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 (1150) the location of the object using the likelihood values for the multiple candidate locations.', '5. The method of claim 1, wherein the multiple candidate locations include locations contained in a field of view (231, 232, 235) of each image of the set of plural images.']