Enhancing anomaly detection in video surveillance with spatio-temporal enhanced deep associative memory networks

Enhanced deep learning model for anomaly object detection and tracking from surveillance videos.

An enhanced wolf Crocuta optimization-based deep Bidirectional Long Short-Term Memory (EnWC-DBiLSTM) classifier is proposed using an enhanced wolf Crocuta optimization-based deep Bidirectional Long Short-Term Memory (EnWC-DBiLSTM) classifier for anomaly object detection and tracking.

Integrated Spatial–Temporal Framework for Video Anomaly Detection in Surveillance Systems

Video-based anomaly detection seeks to discover anomalous events, such as crimes, fires, or medical emergencies, by utilizing both spatial and temporal features of video data. Traditional surveillance systems are frequently limited to minimal recording, requiring human analysts for post-event assessment, resulting in delayed responses during crucial occurrences. To address these issues, we present a multi-layered approach to detecting video anomalies that can deal with both temporal and spatial components of video data. The input video is initially obtained from the dataset and undergoes frame conversion. The extracted key frames are then preprocessed for further analysis. To obtain multi-scale spatial characteristics from each frame, the first layer uses a spatial Pyramid pooling network (SPP-Net) along with a convolutional neural network (CNN). These spatial features are then passed to an optimized bi-directional gated recurrent unit (Opt-Bi-GRU) enhanced with Multi-Head Self-Attention (MHSA), which analyzes the temporal dynamics and captures both forward and backward dependencies across frames. Finally, a capsule network (CapsNet) processes the output of the Bi-GRU, identifying complex patterns that may indicate abnormalities over time. The proposed method is implemented using Python. The proposed model performs better than existing methods in terms of F1-score, specificity, sensitivity, accuracy, recall, precision, FPR, and FNR. The proposed model achieves the highest accuracy of 98.2%, 98.87%, and 98.52%, respectively, utilizing the UBI-fights, UCF-crime, and UCSD pedestrian datasets. These results demonstrate that the proposed framework provides an automated, reliable, and effective solution for real-time anomaly detection in surveillance systems.

A Deep Spatio-Temporal Framework for Multi-Class Traffic Prediction and Accident Detection in Surveillance Video

Traffic surveillance systems play a crucial role in intelligent transportation by enabling automated monitoring, traffic prediction, and accident detection. However, recognizing complex traffic scenarios from real-world videos remains challenging due to dynamic environments and temporal dependencies. This paper proposes a unified spatio-temporal framework that integrates YOLOv8 based object detection, convolutional neural networks for spatial feature ex traction, and long short-term memory networks for temporal modeling. Traffic videos are preprocessed to enhance visual consistency, and detected objects are transformed into structured spatial representations. Temporal dependencies across video sequences are learned using LSTM networks, and the extracted features are evaluated using multiple machine learning classifiers under different preprocessing strategies. Experimental results demonstrate that Z-score standardization improves classification performance, with Support Vector Ma chine achieving 63.27% accuracy and an F1-score of 55.66% in an eight-class traffic scenario classification task, indicating the feasibility and robustness of the proposed framework in real-world traffic environments.

Enhancing video anomaly detection through dual-path reconstruction and diverse pseudo-anomaly generation

FASTe: Framework With Application-Driven Spatio-Temporal Efficiency for Video Anomaly Detection

Video anomaly detection (VAD) plays a crucial role in modern surveillance systems. However, practical deployment remains challenging due to three key limitations: the difficulty of handling variable-length videos, the lack of fine-grained frame-level anomaly localization, and the high computational complexity of using existing models in real-time. To address these challenges, we propose FASTe, a lightweight and spatio-temporal efficient framework for real-time anomaly detection. FASTe introduces 1) a LogSumExp-based multiple instance learning (MIL) aggregation strategy for robust training on variable-length inputs, 2) frame-level anomaly localization under weak supervision, without requiring dense labels, and 3) spatio-temporal decoupling via adaptive pooling, reducing attention complexity from <inline-formula> <tex-math notation="LaTeX">$O(T^{2} \times H^{2} \times W^{2})$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O(T^{2})$ </tex-math></inline-formula>. Evaluated on the UCF-Crime dataset, our approach achieves a receiver operating characteristic area under the curve (ROC-AUC) of 94.57%, achieving improved performance under weak supervision. The proposed framework offers a practical and scalable solution for real-time anomaly detection in resource-constrained surveillance environments.

Transformer enhanced context encoding and stacked autoencoder for video anomaly detection