Advanced Computer Vision and Deep Learning Techniques for Detecting Abnormal Human Behaviour in CCTV Surveillance Systems
DOI:
https://doi.org/10.15662/IJEETR.2026.0802326Keywords:
Computer Vision, Deep Learning, CCTV Surveillance, Abnormal Behaviour Detection, Anomaly Detection, CNN, LSTM, Video Analytics, Smart Surveillance, Artificial IntelligenceAbstract
The rapid expansion of urban infrastructure and public surveillance systems has led to the widespread deployment of Closed-Circuit Television (CCTV) networks for ensuring safety and security. However, continuous monitoring of large volumes of video data by human operators is labour-intensive, error-prone, and inefficient, particularly in identifying subtle or time-critical abnormal behaviours. This necessitates the development of intelligent, automated systems capable of real-time anomaly detection.
This study investigates advanced computer vision and deep learning techniques for detecting abnormal human behaviour in CCTV surveillance systems. The proposed framework integrates video preprocessing, object detection, and spatiotemporal feature extraction using convolutional neural networks (CNNs) and recurrent architectures such as Long Short-Term Memory (LSTM) networks. Special emphasis is placed on identifying deviations from normal behavioural patterns, including suspicious movements, violence, loitering, and unauthorized access, under varying environmental conditions such as low lighting, occlusions, and crowded scenes.
Anomaly detection is performed using both supervised and unsupervised learning approaches, incorporating techniques such as autoencoders and real-time video analytics to enhance detection accuracy and reduce false positives. The system also utilizes motion tracking and behaviourmodelling to improve contextual understanding of activities within surveillance zones. Comparative analysis demonstrates that deep learning–based models provide superior performance in terms of accuracy, scalability, and adaptability when compared to traditional rule-based or manual monitoring methods.
The study concludes that the integration of advanced AI-driven surveillance systems significantly enhances public safety, reduces human workload, and enables proactive threat detection. Furthermore, such systems support the development of smart city infrastructures by enabling efficient, real-time monitoring and rapid response mechanisms. Future research is recommended to improve model robustness, reduce computational complexity, and address privacy and ethical considerations in large-scale deployments
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