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7 articles for “video anomaly detection”
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AI-Powered Face Detection and Recognition Using Machine Learning
Abstract: These days, one of the biggest computer vision technologies is facial recognition. Face identification in computer vision, lighting position, and facial expression is always an extremely challenging issue. In real-time video pictures captured by a video camera, face recognition tracks specific objects. Put simply, it is a system tool that uses a still picture or video frame to automatically identify a person. In this research paper we use different different …
Published in Trends in Machine design · Vol. 11, Issue 2, 2024 · pp. 1–12 Read article
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Automated Suspicious Activity Detection in Video Surveillance Using Deep Learning: A Review
Abstract: In the current era of advanced security systems, video surveillance plays an essential role in ensuring safety by detecting suspicious activities. With the increase in real-time data, manual monitoring has become impractical, paving the way for automated surveillance systems utilizing machine learning (ML) and artificial intelligence (AI) technologies. This paper explores the integration of ML and AI models, specifically convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, for …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 20–27 Read article
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Intensifying Security with Smart Video Surveillance
Abstract: The smart video surveillance system is designed to observe the activities of people nearby for safety concerns. This smart video surveillance can be installed at malls, public areas, banks, businesses, and ATM machines. The field of network surveillance research is rapidly expanding. The cause for this is the global insecurity that is occurring in great extent. As a result, an intelligent monitoring system that gathers data in real time, communicates, …
Published in Recent Trends in Programming languages Read article
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AI-Based Cybersecurity Framework for Protecting Smart Surveillance Infrastructure in Mumbai
Abstract: Smart city infrastructures increasingly rely on interconnected surveillance systems to ensure safety, operational efficiency, and public trust. However, the rapid expansion of IoT-based monitoring technologies has introduced new cyber risks, especially in high-density metropolitan areas. This paper proposes an AI-driven cyber resilience framework targeting smart surveillance infrastructure as a critical smart-living domain, focusing on Mumbai as a case study. Using the CIC-IDS2017 dataset, a machine learning-based intrusion detection model is …
Published in Journal Of Network security · Vol. 14, Issue 1, 2026 Read article
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Real-Time Deepfake Detection in Video Conferencing Systems
Abstract: Deepfake technology presents non-exemplary threats to video conferencing platforms, enabling advanced fraud, impression and misinformation campaigns worth billions annually. Current detection methods either exhibit latencies exceeding 100ms or rely on server-side cloud processing, raising privacy concerns. This paper presents DeepConfGuard, a lightweight hybrid architecture combining MobileNetV2 for spatial feature extraction, a bidirectional LSTM with attention for temporal modelling, and EfficientNetV2 for refinement. It reaches 94.8% accuracy with 85 ms end‑to‑end …
Published in International Journal of Electronics Automation · Vol. 4, Issue 2, 2026 Read article
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Multimodal Generative AI for Vehicular Applications at Edge
Abstract: This paper explores how the rapid advancements in vehicular technology, including autonomous driving and intelligent transportation systems, have driven the need for real-time data processing and decision-making. Multimodal generative AI, when deployed at the edge, offers a powerful solution for vehicular applications by leveraging diverse data sources such as video, audio, sensor inputs, and environmental data. This paper explores the integration of multimodal generative AI with edge computing in vehicular …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 1, 2025 · pp. 27–34 Read article
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Enhancing Campus Security Using IOT Sensors
Abstract: Security within university campuses continues to be of paramount importance to learning institutions, with conventional CCTV systems tending to be slow and prone to operator errors. The project discusses an IoT system with Flutter, ESP32-CAM, flame and noise sensors, and buzzer for an efficient real-time surveillance and timely emergency response. The system can detect abnormal conditions like loud noises and fire risks, sending alarms and enabling live video streaming over …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 2, 2025 · pp. 26–31 Read article