02172nas a2200241   4500000000100000000000100001008004100002260001200043653002200055653002000077653003900097653001800136653002300154653005900177100002600236700001700262245010800279856007900387300001000466490000600476520143400482022001401916       2023                            d  c12/202310aAnomaly Detection10aCrime Detection10aConvolutional Neural Network (CNN)10aDeep Learning10aVideo Surveillance10aConvolutional Gated Recurrent Unit (Convolutional GRU)1 aMaryam Qasim Gandapur1 aElena Verdú00aConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System  uhttps://www.ijimai.org/journal/sites/default/files/2023-11/ijimai8_4_8.pdf  a88-950 v83 aVideo surveillance for real-world anomaly detection and prevention using deep learning is an important and difficult research area. It is imperative to detect and prevent anomalies to develop a nonviolent society. Realworld video surveillance cameras automate the detection of anomaly activities and enable the law enforcement systems for taking steps toward public safety. However, a human-monitored surveillance system is vulnerable to oversight anomaly activity. In this paper, an automated deep learning model is proposed in order to detect and prevent anomaly activities. The real-world video surveillance system is designed by implementing the ResNet-50, a Convolutional Neural Network (CNN) model, to extract the high-level features from input streams whereas temporal features are extracted by the Convolutional GRU (ConvGRU) from the ResNet-50 extracted features in the time-series dataset. The proposed deep learning video surveillance model (named ConvGRUCNN) can efficiently detect anomaly activities. The UCF-Crime dataset is used to evaluate the proposed deep learning model. We classified normal and abnormal activities, thereby showing the ability of ConvGRU-CNN to find a correct category for each abnormal activity. With the UCF-Crime dataset for the video surveillance-based anomaly detection, ConvGRU-CNN achieved 82.22% accuracy. In addition, the proposed model outperformed the related deep learning models.  a1989-1660