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Has files: YesSubject: search.filters.subject.Surveillance

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    PublicationOpen Access
    Advancing Audio Surveillance in Simulated Environments: Real-World Soundscapes and Targeted Noise Detection through Enhanced Beamforming Techniques
    (SLIIT, Faculty of Engineering, 2025-01) Stroud, S; Jones, K; Edwards, G; Robinson, C; Chandler-Crnigoj, S; Ellis, D
    This paper introduces an innovative beamforming approach designed for audio surveillance, executed through a virtual simulation of a real-world environment based at Liverpool John Moores University. Our research is driven by the increasing requirement for sophisticated audio analysis methods to isolate and enhance specific sounds within noisy environments for forensic analysis, for example, in criminal court cases. By leveraging a time-delay beamforming algorithm, our work offers a novel solution to discern and amplify targeted noises amidst complex soundscapes, a challenge commonly encountered in urban surveillance and forensic audio analysis. Our approach's foundation lies in utilising a carefully arranged, robust array of omnidirectional microphones, which are instrumental in capturing a wide range of real-world sound signals. The core of our methodology involves processing captured sounds using the proposed algorithm, followed by evaluating the system's effectiveness in capturing the desired localised audio sources. This paper explores the system's resilience against microphone array degradation, showcasing its robustness in scenarios of partial system functionality. The experiments, grounded in the simulation of real-world acoustic environments, demonstrate the algorithm's adeptness at managing sound reflections and reverberation, critical factors in the realistic replication of urban soundscapes. It also considers the broader implications of our findings, exploring the potential for adopting this technology in various domains beyond law enforcement, including broadcast solutions, advanced audio engineering applications, and animal conservation in the wild. In conclusion, this research showcases a creative approach to audio surveillance and opens the door to numerous applications that can benefit from enhanced methods of audio isolation and analysis. Our findings contribute to the ongoing discourse on developing advanced surveillance technologies, offering insights that could help shape the future of audio processing and analysis.
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    PublicationEmbargo
    COVID-Tracker: Surveillance of Potential Clusters Using a Wristband and Location-based Data
    (IEEE, 2022-07-18) Mandara, A.P. M; Randula, H.K. K; Priyadarshana, H. L.Y; Uyanahewa, J. J.; Manathunga, K; Reyal, S
    COVID-19 is a global pandemic that has threatened the survival of humans and other living beings. COVID-19 causes illnesses varying from the very mild cold to serious health complications resulting in death. Most Information Technology based solutions have been implemented to prevent the COVID-19 pandemic while raising awareness in the public. However, there is a limited number of reliable and real-time applications of self-awareness on COVID-19. Currently, the globe is dealing with the COVID-19 epidemic, particularly in pursuit of economic growth in each country. Therefore, an accurate, efficient automatic method to raise self-awareness by avoiding risky contacts is useful for human survival. This paper describes the automatic detection of temperature using a wearable device and an automatic alerting mechanism to inform the users of potentially risky contacts with higher temperatures nearby within a considerable time frame. COVID-Tracker produces results with high accuracy and efficiency, this is beneficial to improve self-awareness among users, to visualize potential covid clusters, and also to improve the mental health of self-isolated people. The developed application consists of four main components namely: temperature measuring band, mobile application, prediction model-based visualization dashboard and an AI bot. Based on the results reported here, developed methods can help people to achieve self-awareness of COVID-19 by avoiding risk factors early and accurately.
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    PublicationEmbargo
    i-Police-An Intelligent Policing System Through Public Area Surveillance
    (IEEE, 2021-10-27) Jayakody, A; Lokuliyana, S; Dasanayaka, K; Iddamalgoda, A; Ganepola, I; Dissanayake, A
    Technology and law enforcement are now commonly used hand in hand to improve public safety. Most police departments only use CCTV cameras at a few major intersections for remote surveillance. The public is waiting too long for emergency response lines, therefore using new technologies to improve the current policing system has become one of the police's main goals. The paper presents a coordinated framework that could identify the subtleties of violations via an automated public area surveillance system, specifically the weapon-related crimes and vehicle accidents, which are then disassembled, analyzed, and stored for future inspections. The trained models are aimed to reduce the false positives of incident detection. The weapon detection system had the best average precision (93.8%) by using YOLOv5 while the vehicle accident detection system resulted in the best average precision (94.9%) by using YOLOv4. The system is tested against the collected set of CCTV footage and tested how long it takes to create a notification which is the main goal of this system. Notification is generated in less than 5 seconds after an incident is detected. The evidence collection engine developed with MATLAB delivered the expected with an accuracy of 97% making the extracted evidence reliable to both vehicle accidents and crime scenes. Additionally, the framework provides an effective and efficient communication channel through which the residents can report crimes to regular parties.
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    PublicationEmbargo
    A Geophone Based Surveillance System Using Neural Networks and IoT
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Supun Hettigoda, Chamath Jayaminda; Amarathunga, U.; Wijesundara, M.; Wijekoon, J.; Thaha, S.
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.