BAYESIAN METHODOLOGY FOR GEOSPATIAL OBJECT/CHARACTERISTIC DETECTION
FIG7 is a top-view illustration of an example focused observation using a bounding box according to aspects of the disclosure
FIG7 illustrates another example using a bounding box to narrow the candidate locations for object 705. In this example, during review of image 743, which corresponds to field of view 733, a bounding box 760 is drawn around object of interest 705. In this example the object 705 is a fire hydrant. The bounding box 760 corresponds to a narrower slice 762 of the field of view 733. In this regard, candidate locations for which confidence score and normalized values are computed may be limited to locations within the narrower slice 762. Accordingly, locations outside of the slice 762 but still within the field of view 733 may be considered non-observations and used to more precisely locate the object 705. Moreover, computation may be expedited since a reduced number of candidate locations may be analyzed.
view
705
object
762
slice
743
image
760
bounding box
733
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.']