BAYESIAN METHODOLOGY FOR GEOSPATIAL OBJECT/CHARACTERISTIC DETECTION
FIG8 is a top-view illustration of example obstructions according to aspects of the disclosure
As mentioned above, computation of the normalized confidence score may be affected by occlusion, such as when other objects impede a view of an object of interest within an image. FIG8 provides a top-view illustration of example obstructions 871-873, resulting in at least partial occlusion of object 805. The obstructions 871-873 in this example are trees, although occlusion may occur from weather conditions such as fog, or any other objects, such as people, monuments, buildings, animals, etc. When computing the confidence score for an image corresponding to field of view 833, such occlusion may be accounted for. For example, a radial falloff function may be used representing the probability that the object 805 would be visible in that image conditional on it's actually being there. As another example, a non-radial or location-dependent falloff function may account for factors like local population density or vegetation density which would increase the risk of occlusion. Accordingly, the confidence score includes a discount factor reflecting the likelihood of a false negative for reasons other than non-presence, such as occlusion.
view
833
view
805
object
['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.']