Abstract: A system in accordance with one or more embodiments comprises a processor, a processing system operatively connected to the processor, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a decoding of the encoded data to obtain decoded data, provide the decoded data to the processing system. The processing system is programmed to obtain the decoded data from the enhanced networking interface, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying, by a computer vision (CV) node of a plurality of CV nodes, a CV alert; in response to identifying the CV alert: identifying candidate CV nodes of the plurality of CV nodes; generating a dynamic CV zone using the candidate CV nodes; initiating performance of a CV workload by the dynamic CV zone to generate CV data associated with the CV alert; generating a CV alert case associated with the CV alert; obtaining CV data from the dynamic CV zone; updating the CV alert case using the CV data generated during the performance of the CV workload; and providing the updated CV alert case to a VMS.

Abstract: Methods and systems for providing security management services are disclosed. To provide security management services in a manner that reduces the quantity of hardware resources necessary to provide the security management services, the security manager may prioritize security management services based on a trained state of a data processing system to modify a level of surveillance of the persons. By doing so, the device management services may only be provided when certain conditions are met rather than continuously. To place the data processing system in the trained state, the data processing system may need to be trained to predict paths that individuals are likely to traverse using training data. To obtain the training data, previously traversed paths of individuals may be tracked through an environment and stored into a database.

Abstract: Techniques described herein relate to a method for optimizing power for a computer vision environment. The method includes obtaining, by a computer vision (CV) manager, an initial power optimization request associated with a CV workload; in response to obtaining the initial power optimization request: obtaining CV workload information associated with the CV workload; obtaining first CV environment configuration information associated with the power optimization request; generating a power optimization report based on the first CV environment configuration information and the CV workload information using a power optimization model; and initiating performance of the CV workload in a CV environment based on the power optimization report.

Abstract: Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; in response to identifying the CV alert: making a first determination that the CV node is not participating in a distributed workload associated with a higher priority CV alert; in response to the first determination, the CV node: selects candidate CV nodes of the plurality of CV nodes; initiates performance of the distributed CV workload by the candidate CV nodes to generate CV data associated with the CV alert; generates a CV alert case associated with the CV alert; obtains CV data from the candidate CV nodes that are performing the distributed CV workload; updates the CV alert case using the CV data generated during the performance of the distributed CV workload; and provides the updated CV alert case to a VMS.

Abstract: Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; generating a CV alert case associated with the CV alert; identifying nearby CV nodes of the plurality of CV nodes; transmitting CV alert to the nearby CV nodes; for each of the nearby CV nodes: receiving the CV alert; determining, based on CV environment configuration information of the nearby CV node and the CV alert, whether to perform a distributed CV workload; when the determination is to perform the distributed CV workload: initiating performance of the distributed CV workload by the nearby CV nodes to generate CV data; updating the CV alert case using CV data generated during the performance of the distributed CV workload to obtain an updated CV alert case; and transmitting by the nearby CV node to the VMS the updated CV alert case.

Abstract: A method for managing hardware resources comprises obtaining, by a computer vision (CV) manager, a request for a federated CV regression model, in response to the request: performing an initial training of the federated CV regression model using an initial training dataset to obtain an initial federated CV regression model, sending training requests to two local hardware resource systems, wherein each local hardware resource system implements a local camera system and a processing system, and wherein the training request comprises training a local CV regression model based on the processing system and the local camera system, obtaining the first local CV regression model and the second local CV regression model, generating the federated CV regression model using the two local CV regression models, and distributing the federated CV regression model to the first local hardware resource system and the second local hardware resource system.

Abstract: A system includes a processor, a processing system operatively connected to the processor, comprising a decoding-dedicated hardware component, an operating system operating on the processor and not on the processing system, wherein the processing system is programmed to obtain encoded data from a local data source, perform, using the decoding-dedicated hardware component, a decoding of the encoded data to obtain decoded data, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: A system in accordance with one or more embodiments comprises a processor, a processing system operatively connected to the processor, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a decoding of the encoded data to obtain decoded data, provide the decoded data to the processing system. The processing system is programmed to obtain the decoded data from the enhanced networking interface, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: A system comprising a processor, a processing system operatively connected to the processor, a processing system storage, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a metadata analysis of the encoded data to obtain metadata associated with the encoded data, and store the metadata in the processing system storage. The processing system is programmed to obtain the metadata from the processing system storage, perform a computer vision (CV) inferencing on the encoded data using the metadata to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: A system in accordance with one or more embodiments comprises a processor, a processing system operatively connected to the processor, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a decoding of the encoded data to obtain decoded data, provide the decoded data to the processing system. The processing system is programmed to obtain the decoded data from the enhanced networking interface, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: A system includes a processor, a processing system operatively connected to the processor, comprising a decoding-dedicated hardware component, an operating system operating on the processor and not on the processing system, wherein the processing system is programmed to obtain encoded data from a local data source, perform, using the decoding-dedicated hardware component, a decoding of the encoded data to obtain decoded data, perform a computer vision (CV) inferencing on the decoded data to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: A system comprising a processor, a processing system operatively connected to the processor, a processing system storage, and an enhanced networking interface operatively connected to the processing system. The enhanced networking interface is programmed to obtain encoded data from a local data source, perform a metadata analysis of the encoded data to obtain metadata associated with the encoded data, and store the metadata in the processing system storage. The processing system is programmed to obtain the metadata from the processing system storage, perform a computer vision (CV) inferencing on the encoded data using the metadata to obtain inferencing data, provide the inferencing data to the processor, and perform, by the processor, a remediation action based on the inferencing data.

Abstract: A method for managing hardware resources comprises obtaining, by a computer vision (CV) manager, a request for a federated CV regression model, in response to the request: performing an initial training of the federated CV regression model using an initial training dataset to obtain an initial federated CV regression model, sending training requests to two local hardware resource systems, wherein each local hardware resource system implements a local camera system and a processing system, and wherein the training request comprises training a local CV regression model based on the processing system and the local camera system, obtaining the first local CV regression model and the second local CV regression model, generating the federated CV regression model using the two local CV regression models, and distributing the federated CV regression model to the first local hardware resource system and the second local hardware resource system.

Abstract: Methods and systems for managing operation of data processing systems are disclosed. To manage operation of the data processing systems, telemetry data for the data processing systems may be collected and used to estimate the health of the data processing systems. The rates and types of telemetry data this is collected may be dynamically adjusted based on the confidence in estimates for the health of the data processing systems. The collection rate and/or number of monitored quantities may be increased as the confidence in the estimated health of the data processing systems decreases.

Abstract: Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; in response to identifying the CV alert: making a first determination that the CV node is not participating in a distributed workload associated with a higher priority CV alert; in response to the first determination, the CV node: selects candidate CV nodes of the plurality of CV nodes; initiates performance of the distributed CV workload by the candidate CV nodes to generate CV data associated with the CV alert; generates a CV alert case associated with the CV alert; obtains CV data from the candidate CV nodes that are performing the distributed CV workload; updates the CV alert case using the CV data generated during the performance of the distributed CV workload; and provides the updated CV alert case to a VMS.

Abstract: Techniques described herein relate to a method for optimizing power for a computer vision environment. The method includes obtaining, by a computer vision (CV) manager, an initial power optimization request associated with a CV workload; in response to obtaining the initial power optimization request: obtaining CV workload information associated with the CV workload; obtaining first CV environment configuration information associated with the power optimization request; generating a power optimization report based on the first CV environment configuration information and the CV workload information using a power optimization model; and initiating performance of the CV workload in a CV environment based on the power optimization report.

Abstract: Techniques described herein relate to a method for allocating graphics processing unit partitions for a computer vision environment. The method includes obtaining, by a computer vision (CV) manager, an initial graphics processing unit (GPU) partition allocation request associated with a CV workload; in response to obtaining the initial GPU partition allocation request: obtaining CV workload information associated with the CV workload; obtaining first CV environment configuration information associated with the GPU partition allocation request; generating an optimal GPU partition allocation based on the first CV environment configuration information and the CV workload information using a GPU partition model; and initiating performance of the CV workload in a CV environment based on the optimal GPU partition allocation.

Abstract: Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying a CV alert; generating a CV alert case associated with the CV alert; identifying nearby CV nodes of the plurality of CV nodes; transmitting CV alert to the nearby CV nodes; for each of the nearby CV nodes: receiving the CV alert; determining, based on CV environment configuration information of the nearby CV node and the CV alert, whether to perform a distributed CV workload; when the determination is to perform the distributed CV workload: initiating performance of the distributed CV workload by the nearby CV nodes to generate CV data; updating the CV alert case using CV data generated during the performance of the distributed CV workload to obtain an updated CV alert case; and transmitting by the nearby CV node to the VMS the updated CV alert case.

Abstract: Techniques described herein relate to a method for managing a computer vision environment. The method includes identifying, by a computer vision (CV) node of a plurality of CV nodes, a CV alert; in response to identifying the CV alert: identifying candidate CV nodes of the plurality of CV nodes; generating a dynamic CV zone using the candidate CV nodes; initiating performance of a CV workload by the dynamic CV zone to generate CV data associated with the CV alert; generating a CV alert case associated with the CV alert; obtaining CV data from the dynamic CV zone; updating the CV alert case using the CV data generated during the performance of the CV workload; and providing the updated CV alert case to a VMS.