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: A computer implemented method is provided for resource management of stream analytics at each individual node that includes computing a mean of output processing rate of microservices in a pipeline; and evaluating a state of each microservice of the microservices in the pipeline. The computer implemented method also includes selecting a single microservice from the pipeline for updating resources for an action that changes the state in single the microservice that is selected; and performing resource allocation update for the selected microservice. The computer implemented method may also include updating the state of the selected microservice.

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: A method for implementing application self-optimization in serverless edge computing environments is presented. The method includes requesting deployment of an application pipeline on data received from a plurality of sensors, the application pipeline including a plurality of microservices, enabling communication between a plurality of pods and a plurality of analytics units (AUs), each pod of the plurality of pods including a sidecar, determining whether each of the plurality of AUs maintains any state to differentiate between stateful AUs and stateless AUs, scaling the stateful AUs and the stateless AUs, enabling communication directly between the sidecars of the plurality of pods, and reusing and resharing common AUs of the plurality of AUs across different applications.

Abstract: A pull-based communication method for microservices-based real-time streaming video analytics pipelines is provided. The method includes receiving a plurality of frames from a plurality of cameras, each camera including a camera sidecar, arranging a plurality of detectors in layers such that a first detector layer includes detectors with detector sidecars and detector business logic, and the second detector layer includes detectors with only sidecars, arranging a plurality of extractors in layers such that a first extractor layer includes extractors with extractor sidecars and extractor business logic, and the second extractor layer includes extractors with only sidecars, and enabling a mesh controller, during registration, to selectively assign inputs to one or more of the detector sidecars of the first detector layer and one or more of the extractor sidecars of the first extractor layer to pull data items for processing.

Abstract: A method is provided for managing applications for sensors. In one embodiment, the method includes loading a plurality of applications and links for communicating with a plurality of sensors on a platform having an interface for entry of a requested use case; and copying a configuration from a grouping of application instances being applied to a first sensor performing in a function comprising of the requested use case. The method may further include applying the configuration for the grouping of application instances to a second set of sensors to automatically conform the plurality of sensors on the platform to perform the requested use case.

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: Methods and systems for face clustering include determining a quality score for each of a set of input images. A first subset of the input images is clustered, having respective quality scores that exceed a predetermined threshold, to form an initial set of clusters. A second subset of the input images is clustered, having respective quality scores below the predetermined threshold. An action is performed responsive to the clustered images after the second subset is added to the initial set of clusters.

Abstract: Systems and methods for video analytic processing with neuro-symbolic artificial intelligence are provided. These systems and methods include detecting and extracting one or more objects from one or more video frames, and identifying the attributes associated with each of the one or more objects. These further include extracting context from a question, and compiling a series of inquiries to identify the information needed to answer the question and identify missing information. These further include storing intermediate information about the extracted objects and identified attributes, and determining whether the question requires further modeling of data to obtain missing information. These further include mining the one or more video frames for missing information, and compiling the intermediate information from the data storage and missing information based on the context of the question to produce a final answer.

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 managing communications include identifying a system condition in a distributed computing system comprising a first microservice in communication with a second microservice. A communications method is identified responsive to the identified system condition using a reinforcement learning model that associates communication methods with system conditions. The identified communications method is implemented for communications between the first microservice and the second microservice, such that the first microservice and the second microservice use the identified communications method to transmit data.

Abstract: A computer-implemented method executed by at least one processor for detecting tattoos on a human body is presented. The method includes inputting a plurality of images into a tattoo detector, selecting one or more images of the plurality of images including tattoos, extracting, via a feature extractor, tattoo feature vectors from the tattoos found in the one or more images of the plurality of images including tattoos, applying a deep learning tattoo matching model to determine potential matches between the tattoo feature vectors and preexisting tattoo images stored in a tattoo training database, and generating a similarity score between the tattoo feature vectors and one or more of the preexisting tattoo images stored in the tattoo training database.

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: A computer-implemented method is provided. The method includes classifying a video clip of consecutive video frames into one of predefined new classes in relation to a base training set class. The method further includes controlling a system of a motor vehicle for accident avoidance responsive to the one of the predefined classes indicating an impending collision. The classifying step includes extracting video frame features from the video clip. The classifying step further includes aggregating the video frame features of the consecutive video frames into a single frame feature to form a video level feature presentation. The classifying step also includes mapping, by a distance-based classifier, the video level feature presentation into a classification prediction based on cosine similarity.

Abstract: A computer-implemented method executed by at least one processor for reducing radial distortion errors in fish-eye images is presented. The method includes capturing an image from a camera including distortions, detecting arc-shaped edge segments in the image including the distortions, estimating a main distortion parameter by fixing a distortion centerpoint in a middle of the image, estimating the distortion centerpoint with the main distortion parameter, and obtaining an undistorted version of the captured image by inverting the distortion model.

Abstract: Systems and methods for demographic determination using image recognition. The method includes analyzing an image with a pre-trained lightweight neural network model, where the lightweight neural network model generates a confidence value, and comparing the confidence value to a threshold value to determine if the pre-trained lightweight neural network model is sufficiently accurate. The method further includes analyzing the image with a pre-trained heavyweight neural network model for the confidence value below the threshold value, wherein the pre-trained heavyweight neural network model has above about one million trainable parameters and the pre-trained lightweight neural network model has a number of trainable parameters below one tenth the heavyweight model, and displaying demographic data to a user on a user interface, wherein the user modifies store inventory based on the demographic data.

Abstract: A method for implementing application self-optimization in serverless edge computing environments is presented. The method includes requesting deployment of an application pipeline on data received from a plurality of sensors, the application pipeline including a plurality of microservices, enabling communication between a plurality of pods and a plurality of analytics units (AUs), each pod of the plurality of pods including a sidecar, determining whether each of the plurality of AUs maintains any state to differentiate between stateful AUs and stateless AUs, scaling the stateful AUs and the stateless AUs, enabling communication directly between the sidecars of the plurality of pods, and reusing and resharing common AUs of the plurality of AUs across different applications.

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.