Abstract: Systems and methods provide a provisioning framework for a distributed graphics processing unit (GPU) service. A network device in a network receives, from an application, a service request for multi-access edge compute (MEC)-based virtual graphic processing unit (vGPU) services. The network device receives real-time utilization data from multiple MEC clusters in different MEC network locations and generates a utilization view of the multiple MEC clusters in the different MEC network locations. The network device selects, based on the real-time utilization view, one of the different MEC network locations to provide the vGPU services and instructs a of the multiple MEC clusters in the one of the different MEC network locations to perform container provisioning and service provisioning for the vGPU services.

Abstract: Systems and methods provide a provisioning framework for a distributed graphics processing unit (GPU) service. A network device in a network receives, from an application, a service request for multi-access edge compute (MEC)-based virtual graphic processing unit (vGPU) services. The network device receives real-time utilization data from multiple MEC clusters in different MEC network locations and generates a utilization view of the multiple MEC clusters in the different MEC network locations. The network device selects, based on the real-time utilization view, one of the different MEC network locations to provide the vGPU services and instructs a of the multiple MEC clusters in the one of the different MEC network locations to perform container provisioning and service provisioning for the vGPU services.

Abstract: Systems and methods provide a provisioning framework for a distributed graphics processing unit (GPU) service. A network device in a network receives, from an application, a service request for multi-access edge compute (MEC)-based virtual graphic processing unit (vGPU) services. The network device receives real-time utilization data from multiple MEC clusters in different MEC network locations and generates a utilization view of the multiple MEC clusters in the different MEC network locations. The network device selects, based on the real-time utilization view, one of the different MEC network locations to provide the vGPU services and instructs a of the multiple MEC clusters in the one of the different MEC network locations to perform container provisioning and service provisioning for the vGPU services.

Abstract: An exemplary object identification system detects, based on a machine learning model, an object depicted within a video frame. The system identifies, based on the detecting of the object, a class label of the object and a region of interest, within the video frame, of the object. The system identifies, within the region of interest of the object, set of features of the object. The system compares the set of features of the object with a plurality of predefined features within a data store associated with the class label of the object. The system determines, based on the comparing of the set of features of the object with the plurality of predefined features within the data store, whether the object is configured to trigger an augmentation of video content associated with the video frame. Corresponding methods and systems are also disclosed.

Abstract: Systems and methods provide a provisioning framework for a distributed graphics processing unit (GPU) service. A network device in a network receives, from an application, a service request for multi-access edge compute (MEC)-based virtual graphic processing unit (vGPU) services. The network device receives real-time utilization data from multiple MEC clusters in different MEC network locations and generates a utilization view of the multiple MEC clusters in the different MEC network locations. The network device selects, based on the real-time utilization view, one of the different MEC network locations to provide the vGPU services and instructs a of the multiple MEC clusters in the one of the different MEC network locations to perform container provisioning and service provisioning for the vGPU services.

Abstract: An exemplary object identification system detects, based on a machine learning model, an object depicted within a video frame. The system identifies, based on the detecting of the object, a class label of the object and a region of interest, within the video frame, of the object. The system identifies, within the region of interest of the object, set of features of the object. The system compares the set of features of the object with a plurality of predefined features within a data store associated with the class label of the object. The system determines, based on the comparing of the set of features of the object with the plurality of predefined features within the data store, whether the object is configured to trigger an augmentation of video content associated with the video frame. Corresponding methods and systems are also disclosed.

Abstract: An exemplary video generation system (the “system”) receives a plurality of overlapping and concurrently-captured video instances from a plurality of video capture devices in a static configuration. The system temporally synchronizes concurrently-captured video data from each video instance to form a plurality of image sets and identifies a particular image set from the plurality of image sets. The system then performs a camera registration process on the particular image set in order to determine camera registration parameter data. Based on the camera registration parameter data determined using the camera registration process with respect to the particular image set, the system then performs a video stitching process on each of the image sets within the plurality of image sets to form stitched video content representative of a scene captured by the plurality of video capture devices in the static configuration. Corresponding methods and systems are also disclosed.

Abstract: An exemplary video generation system (the “system”) receives a plurality of overlapping and concurrently-captured video instances from a plurality of video capture devices in a static configuration. The system temporally synchronizes concurrently-captured video data from each video instance to form a plurality of image sets and identifies a particular image set from the plurality of image sets. The system then performs a camera registration process on the particular image set in order to determine camera registration parameter data. Based on the camera registration parameter data determined using the camera registration process with respect to the particular image set, the system then performs a video stitching process on each of the image sets within the plurality of image sets to form stitched video content representative of a scene captured by the plurality of video capture devices in the static configuration. Corresponding methods and systems are also disclosed.