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.

Abstract: A computer-implemented method includes obtaining a usecase specification and a usecase runtime specification corresponding to the usecase. The usecase includes a plurality of applications each being associated with a micro-service providing a corresponding functionality within the usecase for performing a task. The method further includes determining that at least one instance of the at least one of the plurality of applications can be reused during execution of the usecase based on the usecase specification and the usecase runtime specification, and reusing the at least one instance during execution of the usecase.

Abstract: A method for free flow fever screening is presented. The method includes capturing a plurality of frames from thermal data streams and visual data streams related to a same scene to define thermal data frames and visual data frames, detecting and tracking a plurality of individuals moving in a free-flow setting within the visual data frames, and generating a tracking identification for each individual of the plurality of individuals present in a field-of-view of the one or more cameras across several frames of the plurality of frames. The method further includes fusing the thermal data frames and the visual data frames, measuring, by a fever-screener, a temperature of each individual of the plurality of individuals within and across the plurality of frames derived from the thermal data streams and the visual data streams, and generating a notification when a temperature of an individual exceeds a predetermined threshold temperature.

Abstract: Systems and methods to specify and execute real-time streaming applications are provided. The method includes specifying an application topology for an application including spouts, bolts, connections, a global hash table, and a topology manager. Each spout receives input data and each bolt transforms the input data, the global hash table allows in memory communication between each spout and bolt to others of the spouts and the bolts. The topology manager manages the application topology. The method includes compiling the application into a shared or static library for applications, and exporting a special symbol associated with the application. The runtime system can be used to retrieve the application topology from the shared or static library based on the special symbol and execute the application topology on a single node or distribute across multiple nodes.

Abstract: Methods and systems for video analysis and response include detecting face images within video streams. Noisy images are filtered from the detected face images. Batches of the remaining detected face images are clustered to generate mini-clusters, constrained by temporal locality. The mini-clusters are globally clustered to generate merged clusters formed of face images for respective people, using camera-chain information to constrain a set of the video streams being considered. Analytics are performed on the merged clusters to identify a tracked individual's movements through an environment. A response is performed to the tracked individual's movements.

Abstract: A computer-implemented method for characterizing a crowd that includes recording a video stream of individuals at a location having at least one reference point for viewing; and extracting the individuals from frames of the video streams. The method may further include assigning tracking identification values to the individuals that have been extracted from the video streams; and measuring at least one type classification from the individuals having the tracking identification values. The method may further include generating a crowd designation further characterizing the individuals having the tracking identification values in the location, the crowd designation comprising at least one measurement of probability that the individuals having the tracking identification values in the location view the at least one reference point for viewing.

Abstract: A computer-implemented method includes obtaining a usecase specification and a usecase runtime specification corresponding to the usecase. The usecase includes a plurality of applications each being associated with a micro-service providing a corresponding functionality within the usecase for performing a task. The method further includes managing execution of the usecase within a runtime system based on the usecase and usecase runtime specifications to perform the task by serving an on-demand query and dynamically scaling resources based on the on-demand query, including using a batch helper server to employ the usecase specification to load dynamic application instances and connect the dynamic application instances to existing instances, and employ a batch helper configuration to load nodes/machines for execution of the on-demand query.

Abstract: Methods and systems for controlling a user interface include identifying a user at a station based on facial recognition of an image of the user's face in a video stream, to match a profile for the user. At least one preference of the user is determined for the display of content, based on the matched profile. Content for the user is configured in accordance with the at least one preference. The configured content is displayed on a user interface of the station.

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: 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: Methods and systems for authentication include determining, at a first worker system, that a master system that stores a current authentication-list cannot be reached by a first network. Authentication is performed on an authentication request using a previously stored copy of the authentication-list at the first worker system. The authentication includes facial recognition that is performed on detected face images for a first time window, before receiving the authentication request, and for a second time window, after receiving the authentication request. Authentication removes matching detected face images after completing an authentication request to prevent other individuals from using a same identifier. Access is granted to a secured area responsive to the authentication.

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 computer-implemented method includes obtaining a usecase specification and a usecase runtime specification corresponding to the usecase. The usecase includes a plurality of applications each being associated with a micro-service providing a corresponding functionality within the usecase for performing a task. The method further includes managing execution of the usecase within a runtime system based on the usecase and usecase runtime specifications to perform the task by serving an on-demand query and dynamically scaling resources based on the on-demand query, including using a batch helper server to employ the usecase specification to load dynamic application instances and connect the dynamic application instances to existing instances, and employ a batch helper configuration to load nodes/machines for execution of the on-demand query.

Abstract: A computer-implemented method includes obtaining a usecase specification and a usecase runtime specification corresponding to the usecase. The usecase includes a plurality of applications each being associated with a micro-service providing a corresponding functionality within the usecase for performing a task. The method further includes determining that at least one instance of the at least one of the plurality of applications can be reused during execution of the usecase based on the usecase specification and the usecase runtime specification, and reusing the at least one instance during execution of the usecase.

Abstract: A system for planning a trip includes heterogeneous data sources including map data, traffic information, vehicle trace data, weather reports, social media data, commuter feedback data, GIS data, travel time data; a stream analytics engine coupled to the heterogeneous data sources; a batch analytics engine coupled to the heterogeneous data sources; and a multi-modal journey planner coupled to the stream analytics engine and the batch analytics engine, the multi-modal journey planner processing indoor travel information and providing real-time updates while a journey is under progress, the multi-modal journey planner providing a journey time forecast as the journey time reflects indoor travel time.