@article{3322,
  keywords = {Anomaly Detection, Crime Detection, Convolutional Neural Network (CNN), Deep Learning, Video Surveillance, Convolutional Gated Recurrent Unit (Convolutional GRU)},
  author = {Maryam Qasim Gandapur and Elena Verdú},
  title = {ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System},
  abstract = {Video 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.},
  year = {2023},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {8},
  chapter = {88},
  number = {4},
  pages = {88-95},
  month = {12/2023},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2023-11/ijimai8_4_8.pdf},
  doi = {10.9781/ijimai.2023.05.006},
}