Automated Suspicious Activity Detection in Video Surveillance Using Deep Learning: A Review - Prati Dubey; Rakesh Kumar Mittan (International Journal of Optical Innovations & Research, 2025)
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 suspicious human activity detection in video streams. The proposed system involves video data collection, preprocessing, feature extraction, and model training to identify abnormal behaviour. We review existing literature on human activity detection, discussing several models and techniques for video anomaly detection and object tracking. The study demonstrates how these intelligent systems can be applied in various environments, providing real-time insights and proactive security measures. The paper also highlights the challenges related to deep learning, such as overfitting, computational requirements, and the need for extensive labelled data for effective model training.
Keywords: Machine Learning; Suspicious activity detection; Video Surveillance; convolutional neural networks (CNN); long short-term memory (LSTM) networks