Abstract: A cross-sensor object-attribute analysis method for detecting at least one object in a space by using cooperation of a plurality of image sensing devices, the method including: the image sensing devices sending raw data or attribute vector data of sensed multiple images to a main information processing device, where the raw data and attribute vector data all correspond to a time record; and the main information processing device generating one or more of the attribute vectors according to the raw data of each of the images and using each of the one or more of the attribute vectors to correspond to one of the at least one object, or the main information processing device directly using the attribute vector data to correspond to the at least one object.

Abstract: A reinforcement learning system is provided, including at least one agent and a server. According to a set condition, the at least one agent transmits a plurality of state sets related to a state of the environment through a network, receives a plurality of action sets for performing an action, and transmits, to the server, a plurality of feedback messages generated after interacting with the environment. The server configures a predetermined ratio of the memory space as at least one workstation according to the set condition, and selects an untrained model to be temporarily stored in the at least one workstation. The at least one workstation imports a current state set, a current action set, and a current feedback message, as parameters, into the untrained model for reinforcement learning, and generates a next action set until a goal is achieved.

Abstract: A cross-sensor object-space correspondence analysis method for detecting at least one object in a space by using cooperation of a plurality of image sensing devices, the method including: the image sensing devices sending raw data or grid code data of multiple frames of sensed images to a main information processing device to determine a corresponding projection point or a moving track of each of the at least one object on a reference plane corresponding to the space, where each of the image sensing devices has an image plane, and the raw data and each of the grid code data all correspond to a time record.

Abstract: A cross-sensor object-space correspondence analysis method for detecting at least one object in a space by using cooperation of a plurality of image sensing devices, the method including: the image sensing devices sending raw data or grid code data of multiple frames of sensed images to a main information processing device to determine a corresponding projection point or a moving track of each of the at least one object on a reference plane corresponding to the space, where each of the image sensing devices has an image plane, and the raw data and each of the grid code data all correspond to a time record.

Abstract: A space-based cross-sensor object positioning and identification method for detecting at least one object in a space, including: periodically performing an object bounding box defining process on raw data of an image sensed by each of a plurality of image sensing devices to generate at least one bounding box for at least one aforementioned object, and performing a first inference process and a second inference process on each aforementioned bounding box to generate a grid code and an attribute vector respectively; and performing a third inference process on plural combined data sets of the grid code and the attribute vector deduced from the images sensed by the image sensing devices to map at least one aforementioned combined data set attributed to a same identity to a local area on a reference plane of the space.

Abstract: A cross-sensor object-attribute analysis method for detecting at least one object in a space by using cooperation of a plurality of image sensing devices, the method including: the image sensing devices sending raw data or attribute vector data of sensed multiple images to a main information processing device, where the raw data and attribute vector data all correspond to a time record; and the main information processing device generating one or more of the attribute vectors according to the raw data of each of the images and using each of the one or more of the attribute vectors to correspond to one of the at least one object, or the main information processing device directly using the attribute vector data to correspond to the at least one object.