Browsing by Author "Singh, Gurpreet"
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Item Open Access Reducing viral transmission through AI-based crowd monitoring and social distancing analysis(IEEE, 2022-10-13) Fraser, Benjamin; Copp, Brendan; Singh, Gurpreet; Keyvan, Orhan; Bian, Tongfei; Sonntag, Valentin; Xing, Yang; Guo, Weisi; Tsourdos, AntoniosThis paper explores multi-person pose estimation for reducing the risk of airborne pathogens. The recent COVID-19 pandemic highlights these risks in a globally connected world. We developed several techniques which analyse CCTV inputs for crowd analysis. The framework utilised automated homography from pose feature positions to determine interpersonal distance. It also incorporates mask detection by using pose features for an image classification pipeline. A further model predicts the behaviour of each person by using their estimated pose features. We combine the models to assess transmission risk based on recent scientific literature. A custom dashboard displays a risk density heat-map in real time. This system could improve public space management and reduce transmission in future pandemics. This context agnostic system and has many applications for other crowd monitoring problems.Item Open Access A two-stages unsupervised/supervised statistical learning approach for drone behaviour prediction(IEEE, 2023-10-24) Singh, Gurpreet; Perrusquía, Adolfo; Guo, WeisiDrones are prone to abuse due to their low cost and their pool of potential illegal applications that can compromise safety of national infrastructures and facilities. Hence, drone detection and predict its behaviour is crucial to ensure smooth operation of services. In this paper, an unsupervised/supervised statistical learning algorithm for drone behaviour prediction is proposed. The algorithm is based on drone detection data collected from any radar or RF- sensor. The architecture of the approach is comprised of two stages: i) the first stage attempts to study the drone detection data using either unsupervised or supervised learning methods to model low dimensional expert’s features, and ii) in the second stage a real time drone behaviour predictor model is proposed based on the Kolmogorov-Smirnov and Wasserstein distances. Simulation studies using synthetic data obtained from the AirSim simulator are given to provide the evidence-base for future improvements in the field of drone behaviour prediction.