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

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

The proposed STEAD-network combines various techniques, including spatio-temporal enhancement, associative memory modules, and pattern recognition, to effectively capture and recognize abnormal events, and consistently outperforms other methods in anomaly detection accuracy across all datasets.

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.

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

Action-aware anomaly detection in surveillance videos using normal behavior modeling

Detecting anomalous events in surveillance videos is a critical yet challenging task due to the rarity, diversity, and unpredictable nature of abnormal activities. Existing methods often rely on fully supervised annotations or weakly supervised multiple instance learning frameworks that require labeled anomalous videos and complex training strategies. In this work, we propose AAAD (Action-Aware Anomaly Detection), a framework that models normal human behavior using learned action embeddings and detects anomalies as semantic deviations. The proposed method first segments videos into fixed-length clips and extracts compact action embeddings using a frozen ResNet18 backbone. An autoencoder is then trained exclusively on embeddings derived from normal clips, enabling the model to learn the distribution of normal actions without requiring anomaly labels. During inference, anomalies are detected based on reconstruction error in the embedding space, allowing temporal localization of abnormal events at the clip level. Experiments conducted on the UCF-Crime dataset demonstrate that the proposed approach effectively distinguishes normal and abnormal activities, achieving 69.0% AUC with a separation ratio of 1.25x. Our method outperforms unsupervised baselines including k-NN (56.4%), One-Class SVM (60.5%), Isolation Forest (54.5%), and Clustering (57.0%). Qualitative and quantitative results confirm that modeling normal action semantics provides a robust and scalable solution for real-world surveillance anomaly detection.

Transformer enhanced context encoding and stacked autoencoder for video anomaly detection

Object-Centric Industrial Video Anomaly Detection with Local-Global Representation Learning

A novel framework centered on object-centric video anomaly detection, heavily augmented by a local-global representation learning mechanism, suggesting that integrating structured object interactions into representation learning provides a highly scalable and robust solution for real-world industrial monitoring.