Space-based cross-sensor object positioning and identification method and system

A space-based cross-sensor object positioning and identification system, which has an edge computing architecture including a main information processing device and a plurality of information processing units respectively disposed in a plurality of image sensing devices installed in a space, and the edge computing architecture is used to execute a space-based cross-sensor object positioning and identification method for detecting at least one object in a space by using cooperation of the image sensing devices, and the method includes:

• periodically receiving raw data of a plurality of images sensed by the image sensing devices;
• performing an object bounding box defining process on raw data of the image sensed by each of the image sensing devices to generate at least one bounding box of at least one of the at least one object, and performing a first inference process and a second inference process on each said bounding box to generate a grid code and an attribute vector respectively, and storing the grid code and the attribute vector in a memory in a related manner;
• and
• performing a third inference process on plural combined data sets of the grid code and the attribute vector deduced from the images of the image sensing devices to map at least one said combined data set determined to belong to a same identity to a local area on a reference plane of the space;
• wherein the first inference process includes:
• dividing the reference plane into a plurality of grids and setting a plurality of different grid codes for the grids, performing a central-point calculation on one said bounding box to find a projection point on the reference plane, and using a look-up table to find a corresponding said grid code for the projection point;
• the second inference process includes:
• using a first AI module to perform an attribute evaluation calculation on one said bounding box to determine one said attribute vector;
• and the third inference process includes:
• using a second AI module to perform an identity evaluation calculation on the attribute vectors to determine at least one said identity, and mapping at least one said combined data set of the grid code and the attribute vector that corresponds to one said identity to one said local area on the reference plane.