dc.creator | Qasim Gandapur, Maryam | |
dc.creator | Verdú, Elena | |
dc.date.accessioned | 2023-06-01T10:12:22Z | |
dc.date.accessioned | 2023-09-07T15:20:11Z | |
dc.date.available | 2023-06-01T10:12:22Z | |
dc.date.available | 2023-09-07T15:20:11Z | |
dc.date.created | 2023-06-01T10:12:22Z | |
dc.identifier | 1989-1660 | |
dc.identifier | https://reunir.unir.net/handle/123456789/14812 | |
dc.identifier | https://doi.org/10.9781/ijimai.2023.05.006 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8732135 | |
dc.description.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. | |
dc.language | eng | |
dc.publisher | International Journal of Interactive Multimedia and Artificial Intelligence | |
dc.relation | ;In Press | |
dc.relation | https://www.ijimai.org/journal/bibcite/reference/3322 | |
dc.rights | openAccess | |
dc.subject | anomaly detection | |
dc.subject | crime detection | |
dc.subject | Convolutional Neural Network (CNN) | |
dc.subject | deep learning | |
dc.subject | video surveillance | |
dc.subject | Convolutional Gated Recurrent Unit (Convolutional GRU) | |
dc.subject | IJIMAI | |
dc.title | ConvGRU-CNN: Spatiotemporal Deep Learning for Real-World Anomaly Detection in Video Surveillance System | |
dc.type | article | |