BAYESIAN METHODOLOGY FOR GEOSPATIAL OBJECT/CHARACTERISTIC DETECTION
FIG9 is a top-view illustration of an example probability of obstructions affecting observations according to aspects of the disclosure
A likelihood of occlusion may increase as a distance between an image capture location and an object of interest increases. FIG9 illustrates an example probability of obstructions affecting observations. Object 905 is within a field of view 933 of an image captured from location 923. The field of view 933 is subdivided into different regions 982, 984, 986 based on the probability of occlusion. For example, for any objects within first region 982, which is closest to the image capture location 923, the probability of occlusion is lowest. For objects within second region 984, such as object 905, the probability of occlusion is higher than for the first region 982. For region 986, which is furthest from the image capture location 923, the risk of occlusion is greatest. In some examples, the probabilities may be adjusted using iterative training based on observations.
view
982
first region
986
region
923
location
905
Object
984
objects within second region
933
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.']