Abstract: A method for rapid discovery of satellite behavior, applied to a pursuit-evasion system including at least one satellite and a plurality of space sensing assets. The method includes performing transfer learning and zero-shot learning to obtain a semantic layer using space data information. The space data information includes simulated space data based on a physical model. The method further includes obtaining measured space-activity data of the satellite from the space sensing assets; performing manifold learning on the measured space-activity data to obtain measured state-related parameters of the satellite; modeling the state uncertainty and the uncertainty propagation of the satellite based on the measured state-related parameters; and performing game reasoning based on a Markov game model to predict satellite behavior and management of the plurality of space sensing assets according to the semantic layer and the modeled state uncertainty and uncertainty propagation.

Abstract: A method for rapid discovery of satellite behavior, applied to a pursuit-evasion system including at least one satellite and a plurality of space sensing assets. The method includes performing transfer learning and zero-shot learning to obtain a semantic layer using space data information. The space data information includes simulated space data based on a physical model. The method further includes obtaining measured space-activity data of the satellite from the space sensing assets; performing manifold learning on the measured space-activity data to obtain measured state-related parameters of the satellite; modeling the state uncertainty and the uncertainty propagation of the satellite based on the measured state-related parameters; and performing game reasoning based on a Markov game model to predict satellite behavior and management of the plurality of space sensing assets according to the semantic layer and the modeled state uncertainty and uncertainty propagation.

Abstract: Method and system for detecting and tracking multiple moving targets based on wide-area motion imagery are provided. The method includes receiving a task request from a client; sending the task request to a local agent; acquiring to-be-processed images from the client, in response to the task request; calling an Hadoop API and checking a running status of a Hadoop system for an available resource in the Hadoop system; when a resource in the Hadoop system is available, pushing the to-be processed images to a back-end Hadoop distributed file system (HDFS); running tracking algorithms of multiple moving objects associated with the to-be-processed images to provide tracking results of the multiple moving objects; when the Hadoop system is completed in running the tracking algorithms, generating and sending a message to the local agent; and sending the tracking results of the multiple moving objects from the HDFS to the front-end database.

Abstract: Method and system for detecting and tracking multiple moving targets based on wide-area motion imagery are provided. The method includes receiving a task request from a client; sending the task request to a local agent; acquiring to-be-processed images from the client, in response to the task request; calling an Hadoop API and checking a running status of a Hadoop system for an available resource in the Hadoop system; when a resource in the Hadoop system is available, pushing the to-be processed images to a back-end Hadoop distributed file system (HDFS); running tracking algorithms of multiple moving objects associated with the to-be-processed images to provide tracking results of the multiple moving objects; when the Hadoop system is completed in running the tracking algorithms, generating and sending a message to the local agent; and sending the tracking results of the multiple moving objects from the HDFS to the front-end database.

Abstract: In accordance with various embodiments of the disclosed subject matter, a method and a system for detecting multiple objects from real-time images are provided. The method comprises: performing, using a CPU host, an image segmentation process to divide real-time input images into a plurality of image partitions; performing, by multiple GPUs, a fast block-wise registration process, a mark setting process, a background generation process, a foreground generation process based on a Hyper-Q computation infrastructure, and a support vector machine classification process; and generating, by the CPU host, visualization classification images.

Abstract: In accordance with various embodiments of the disclosed subject matter, a method and a system for detecting multiple objects from real-time images are provided. The method comprises: performing, using a CPU host, an image segmentation process to divide real-time input images into a plurality of image partitions; performing, by multiple GPUs, a fast block-wise registration process, a mark setting process, a background generation process, a foreground generation process based on a Hyper-Q computation infrastructure, and a support vector machine classification process; and generating, by the CPU host, visualization classification images.