Intelligent Video Surveillance

Abstract

The rapid growth of large-scale surveillance infrastructures has created an urgent requirement for automated techniques capable of analysing continuous video streams in real time. Conventional surveillance systems depend almost entirely on prolonged human observation, which is inherently inefficient and vulnerable to fatigue, delayed response, and missed critical incidents. This paper presents an intelligent video surveillance framework based on an unsupervised deep learning approach for detecting abnormal activities in video streams. The proposed system employs a Convolutional Long Short-Term Memory (Conv-LSTM) autoencoder to model normal spatio-temporal patterns in surveillance videos and to identify deviations through reconstruction error analysis. The network is trained exclusively on normal activity sequences, enabling the detection of unforeseen abnormal events without requiring explicit anomaly labels. The complete framework includes a real-time preprocessing pipeline, sequence buffering mechanism, threshold-based decision logic, and visual alert generation. Experimental evaluation conducted on a standard benchmark dataset demonstrates that the system can effectively identify abnormal behaviours such as running and object throwing while maintaining a low false-alarm rate. The results confirm the suitability of the proposed framework for practical real-time intelligent surveillance applications.