Abstract: A method for employing a semi-supervised learning approach to improve accuracy of a small model on an edge device is presented. The method includes collecting a plurality of frames from a plurality of video streams generated from a plurality of cameras, each camera associated with a respective small model, each small model deployed in the edge device, sampling the plurality of frames to define sampled frames, performing inference to the sampled frames by using a big model, the big model shared by all of the plurality of cameras and deployed in a cloud or cloud edge, using the big model to generate labels for each of the sampled frames to generate training data, and training each of the small models with the training data to generate updated small models on the edge device.

Abstract: Methods and systems for executing an application include extending a container orchestration system application programming interface (API) to handle objects that specify components of an application. An application representation is executed using the extended container orchestration system API, including the instantiation of one or more services that define a data stream path from a sensor to a device.

Abstract: Systems and methods are provided for deploying applications within a wireless network infrastructure, including initiating, by a centralized control module in a pre-configured hardware unit having a 5G wireless communication module, edge computing device, centralized control module, and data processing module with access to cloud resources, a setup procedure upon receiving a deployment command, the setup procedure including activating the 5G wireless communication module to establish a network connection. User equipment for communication with sensors and cameras is deployed using an edge device through the network connection. Application deployment is managed using a centralized control module including an edge cloud optimizer for allocating resources between an edge computing device and the cloud resources based on real-time analysis of network conditions and application requirements.

Abstract: A method for real-time cross-spectral object association and depth estimation is presented. The method includes synthesizing, by a cross-spectral generative adversarial network (CS-GAN), visual images from different data streams obtained from a plurality of different types of sensors, applying a feature-preserving loss function resulting in real-time pairing of corresponding cross-spectral objects, and applying dual bottleneck residual layers with skip connections to accelerate real-time inference and to accelerate convergence during model training.

Abstract: Methods and systems for camera configuration include configuring an image capture configuration parameter of a camera according to a multi-objective reinforcement learning aggregated reward function. Respective quality estimates for analytics are determined after configuring the image capture parameters. The aggregated reward function is updated based on the quality estimates.

Abstract: Systems and methods are provided for dynamically tuning camera parameters in a video analytics system to optimize analytics accuracy. A camera captures a current scene, and optimal camera parameter settings are learned and identified for the current scene using a Reinforcement Learning (RL) engine. The learning includes defining a state within the RL engine as a tuple of two vectors: a first representing current camera parameter values and a second representing measured values of frames of the current scene. Quality of frames is estimated using a quality estimator, and camera parameters are adjusted based on the quality estimator and the RL engine for optimization. Effectiveness of tuning is determined using perceptual Image Quality Assessment (IQA) to quantify a quality measure. Camera parameters are adaptively tuned in real-time based on learned optimal camera parameter settings, state, quality measure, and set of actions, to optimize the analytics accuracy for video analytics tasks.

Abstract: Methods and systems for transmission over a heterogeneous network include determining a path through a first network, including collecting quality of service (QoS) parameters for network devices in the path. The QoS parameters of the network devices are configured to provide a predetermined QoS assurance across the path. A network flow is transmitted from a network slice of a second network through the first network, along the path.

Abstract: Systems and methods are provided for increasing accuracy of video analytics tasks in real-time by acquiring a video using video cameras, and identifying fluctuations in the accuracy of video analytics applications across consecutive frames of the video. The identified fluctuations are quantified based on an average relative difference of true-positive detection counts across consecutive frames. Fluctuations in accuracy are reduced by applying transfer learning to a deep learning model initially trained using images, and retraining the deep learning model using video frames. A quality of object detections is determined based on an amount of track-ids assigned by a tracker across different video frames. Optimization of the reduction of fluctuations includes iteratively repeating the identifying, the quantifying, the reducing, and the determining the quality of object detections until a threshold is reached. Model predictions for each frame in the video are generated using the retrained deep learning model.

Abstract: Methods and systems for reserving resources include determining a state of a distributed computing system based on resource needs of an application that is executed on the distributed computing system and system resource constraints. An action is determined using the state of the distributed computing system as an input to a trained reinforcement learning model. A resource request is issued for the application to reserve resources based on the action.

Abstract: Systems and methods for network bandwidth optimization, including transmitting sensor data from one or more sensors over a wireless network into a generated network slice, submitting a Quality-of-Service (QoS) request for one or more applications by specifying desired network slice characteristics, and predicting network bandwidth needed for granting the QoS request for the one or more applications using a cost function based on magnitude, direction, and frequency of error. Time-varying network bandwidth usage is continuously monitored, and new QoS requests for the one or more applications are periodically requested based on the monitoring. An updated prediction for updated bandwidth needed for the new QoS request is generated using the cost function, and network bandwidth reservations are iteratively adjusted based on the updated prediction for the new QoS request to provide an amount of network resources to the one or more applications to support the new QoS request.

Abstract: Systems and methods for determining dwell time is provided. The method includes receiving images of an area including one or more people from one or more cameras, and detecting a presence of each of the one or more people in the received images using a worker. The method further includes receiving by the worker digital facial features stored in a watch list from a master controller, and performing facial recognition and monitoring the dwell time of each of the one or more people. The method further includes determining if each of the one or more people is in the watch list or has exceeded a dwell time threshold.

Abstract: Systems and methods for network bandwidth optimization, including transmitting sensor data from one or more sensors over a wireless network into a generated network slice, submitting a Quality-of-Service (QoS) request for one or more applications by specifying desired network slice characteristics, and predicting network bandwidth needed for granting the QoS request for the one or more applications using a cost function based on magnitude, direction, and frequency of error. Time-varying network bandwidth usage is continuously monitored, and new QoS requests for the one or more applications are periodically requested based on the monitoring. An updated prediction for updated bandwidth needed for the new QoS request is generated using the cost function, and network bandwidth reservations are iteratively adjusted based on the updated prediction for the new QoS request to provide an amount of network resources to the one or more applications to support the new QoS request.

Abstract: Methods and systems for executing an application include extending a container orchestration system application programming interface (API) to handle objects that specify components of an application. An application representation is executed using the extended container orchestration system API, including the instantiation of one or more services that define a data stream path from a sensor to a device.

Abstract: A method for performing resource orchestration for microservices-based 5G applications in a dynamic, heterogenous, multi-tiered compute and network environment is presented.

Abstract: A method for automatically adjusting camera parameters to improve video analytics accuracy during continuously changing environmental conditions is presented. The method includes capturing a video stream from a plurality of cameras, performing video analytics tasks on the video stream, the video analytics tasks defined as analytics units (AUs), applying image processing to the video stream to obtain processed frames, filtering the processed frames through a filter to discard low-quality frames and dynamically fine-tuning parameters of the plurality of cameras. The fine-tuning includes passing the filtered frames to an AU-specific proxy quality evaluator, employing State-Action-Reward-State-Action (SARSA) reinforcement learning (RL) computations to automatically fine-tune the parameters of the plurality of cameras, and based on the reinforcement computations, applying a new policy for an agent to take actions and learn to maximize a reward.

Abstract: A method for specifying and executing an application including multiple microservices on 5G slices within a multi-tiered 5G infrastructure is presented. The method includes managing compute requirements and network requirements of the application simultaneously by determining end-to-end application characteristics by employing an application slice specification including an application ID component, an application name component, an application metadata component, a function dependencies component, a function instances component, and an instance connections component, specifying a function slice specification including a function network slice specification and a function compute slice specification, and employing a runtime component including a resource manager, an application slice controller, and an application slice monitor, wherein the resource manager maintains a database and manages starting, stopping, updating, and deleting application instances.

Abstract: A method for tracing individuals through physical spaces that includes registering cameras in groupings relating a physical space. The method further includes performing local video monitoring including a video sensor input that outputs frames from inputs from recording with the cameras in the groupings, a face detection application for extracting faces from the output frames, and a face matching application for matching faces extracted from the output frames to a watchlist, and a local movement monitor that assigns tracks to the matched faces. The method further includes performing a global monitor including a biometrics monitor for preparing the watchlist of faces, the watchlist of faces being updated when a new face is detected by the cameras in the groupings, and a global movement monitor that combines the outputs from the assigned tracks to the matched faces to launch a report regarding individual population traveling to the physical spaces.

Abstract: A method for employing a semi-supervised learning approach to improve accuracy of a small model on an edge device is presented. The method includes collecting a plurality of frames from a plurality of video streams generated from a plurality of cameras, each camera associated with a respective small model, each small model deployed in the edge device, sampling the plurality of frames to define sampled frames, performing inference to the sampled frames by using a big model, the big model shared by all of the plurality of cameras and deployed in a cloud or cloud edge, using the big model to generate labels for each of the sampled frames to generate training data, and training each of the small models with the training data to generate updated small models on the edge device.

Abstract: A computer-implemented method for emulating an object recognizer includes receiving testing image data, and emulating, by employing a first object recognizer, a second object recognizer. Emulating the second object recognizer includes using the first object recognizer to perform object recognition on a testing object from the testing image data to generate data, the data including a feature representation for the testing object, and classifying the testing object based on the feature representation and a machine learning model configured to predict whether the testing object would be recognized by a second object recognizer. The method further includes triggering an action to be performed based on the classification.

Abstract: A method for real-time cross-spectral object association and depth estimation is presented. The method includes synthesizing, by a cross-spectral generative adversarial network (CS-GAN), visual images from different data streams obtained from a plurality of different types of sensors, applying a feature-preserving loss function resulting in real-time pairing of corresponding cross-spectral objects, and applying dual bottleneck residual layers with skip connections to accelerate real-time inference and to accelerate convergence during model training.