BAYESIAN METHODOLOGY FOR GEOSPATIAL OBJECT/CHARACTERISTIC DETECTION
FIG2 is a top-view illustration of example observations and non-observations according to aspects of the disclosure
FIG2 is a top-view illustration of example observations and non-observations of a particular object 205. In this example, an image capture device moving along a roadway 210 captures images at each of a plurality of capture locations 221-225 along the roadway 210. Each image has an associated field of view 23-235. For example, based on a position and angle of the image capture device at capture location 221, it captures imagery of everything within field of view 231. Similarly, at subsequent capture location 223, the image capture device would capture imagery of objects within field of view 233. When searching for the particular object 205, fields of view 233 and 234, which include the object 205, are considered observations of that object. Fields of view 231, 232, and 235, which do not include the object 205, are considered non-observations.
view
233
view
210
roadway
221
device at capture location
223
capture location
234
view
231
view
205
object
224
capture location
['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.']