GhostBottleneck-ConvNeXt-based spatial–temporal framework for video anomaly detection in surveillance systems

The detection of anomalies within video surveillance becomes a key issue in terms of public safety due to the rapid development of surveillance camera deployment. Hence, the urgent need arises for automatic anomaly detection techniques for identifying unusual behaviours such as accidents, aggressive situations, and fires. Simultaneously, the increasing complexity of video analysis and diversity of behavioural characteristics place severe limitations on the effectiveness of the existing methods of anomaly detection, which usually suffer from low performance due to poor spatial-temporal feature representation. To resolve these problems, an innovative deep learning (DL)-based approach is presented. Initially, the input frames are preprocessed using standard methods like image resizing and normalisation to ensure uniformity and improved learning efficiency. For spatial – temporal feature extraction, a 3D Convolutional Neural Network (3DCNN) is utilised to capture both spatial and short-term motion information. Subsequently, a Bidirectional Long Short-Term Memory (BiLSTM) network integrated with an additive attention mechanism (AMM) is utilised to obtain long-range temporal dependencies and emphasise significant features across the sequence. Finally, a novel GhostBottleneck-ConvNeXt (GBConvNeXt) architecture is introduced for efficient and accurate classification of anomalous events. The proposed approach outperforms existing methods in accuracy, recall, precision, specificity, sensitivity, F-measure, FPR, and FNR.