International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 01 | Jan 2025
p-ISSN: 2395-0072
www.irjet.net
AI-POWERED HUMAN SUSPICIOUS AND ANOMALOUS ACTIVITY MONITORING SYSTEM Prof. Mithuna H R1, Srinidhi Rao2, Shilpa M S3, Sindhu B Hulagur4, Maithri K5 1 Assistant Professor, ISE, Acharya Institute Of Technology, Karnataka, India 2 B.E Student, ISE, Acharya Institute Of Technology, Karnataka, India
B.E Student, ISE, Acharya Institute Of Technology, Karnataka, India B.E Student, ISE, Acharya Institute Of Technology, Karnataka, India 5 B.E Student, ISE, Acharya Institute Of Technology, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------3
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Abstract - In response to heightened global security
highly accurate and reliable, offering a scalable solution for enhancing public safety.
challenges, this study introduces an intelligent surveillance framework designed to autonomously detect suspicious and anomalous human behaviors in real-world settings. The proposed Human Suspicious and Anomalous Activity Monitoring System (HSAAMS) integrates cutting-edge artificial intelligence methodologies, combining computer vision with hybrid machine learning architectures to address limitations in conventional surveillance systems, such as delayed response times and high false-alarm rates. This research contributes a scalable, context-aware solution for proactive security management, demonstrating viability in smart cities and critical infrastructure while balancing civil liberties. Real-world deployments validate scalability, with dynamic recalibration enhancing performance in crowded or low-visibility settings. The system’s interpretability—achieved via attention heatmaps and counterfactual explanations— supports transparent decision-making for security operators. By harmonizing AI-driven analytics with privacy safeguards, this work addresses critical gaps in modern surveillance systems.
2. LITERATURE REVIEW Recent years have witnessed remarkable progress in AIdriven surveillance technologies, spurred by growing demands for improved security across diverse sectors. Conventional approaches like CCTV cameras and human monitoring frequently lack the capability for real-time insights or preemptive responses. This gap has fueled the creation of advanced AI systems that employ machine learning and computer vision to detect and analyze suspicious activities autonomously. Research has extensively investigated the application of deep learning architectures, such as CNNs for spatial pattern recognition and RNNs for temporal analysis, in identifying behavioral anomalies. For example, Tripathi et al. (2018) demonstrated how these models effectively flag unusual actions in academic environments, such as unauthorized movements during exams. Alsabhan (2023) further explored machine learning combined with LSTM networks to detect academic dishonesty in universities, analyzing patterns like irregular eye movements or atypical device usage. Unsupervised techniques, including auto encoders and GANs, have also enhanced anomaly detection by learning normal behavior patterns, thereby reducing false alarms in systems like automated exam proctoring tools. Masud et al. (2022) illustrated this with an AI-powered proctoring tool that identifies suspicious actions during online tests, such as unauthorized resource access. However, the rise of AI surveillance raises significant ethical and privacy challenges. Experts stress the need for safeguards like strict data anonymization, algorithmic transparency, and user consent protocols to prevent misuse and protect individual rights. In conclusion, while AI surveillance systems show immense potential in boosting security through intelligent monitoring, their adoption must balance innovation with ethical frameworks to address societal concerns.
Key Words: Human Suspicious and Anomalous Activity Monitoring System(HSAAMS),Convolutional Neural Networks(CNNs),Suspicious Human Behaviors, Proactive Security Management, High False Alarm Rates.
1.INTRODUCTION In our rapidly evolving world, security concerns have become a significant part of everyday life. Traditional surveillance methods often struggle to keep up with the increasing demand for real-time analysis and proactive threat detection. This is where artificial intelligence steps in, offering unparalleled capabilities to enhance security measures. This paper introduces an AI-powered Human Suspicious and Anomalous Activity Monitoring System, designed to detect and analyze unusual behaviors in real-time. Leveraging advanced machine learning algorithms and computer vision techniques, the system aims to identify suspicious activities with high precision. The architecture includes data acquisition, pre-processing, feature extraction, and anomaly detection modules, all working together seamlessly. Through extensive experimentation, the system has proven to be
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3. RELATED WORKS Recent advancements in video analytics have enabled researchers to detect anomalous activities across domains
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