Video anomaly detection system using deep convolutional and recurrent models

Abstract

Automatic identification of anomalies in video surveillance is an interesting research field. Even though interactive multimedia anomaly detection algorithms have been developed, it is still hard for video surveillance to find unusual things like illegal activities and crimes. In this study, a deep convolutional neural network (CNN) and a simple recurrent unit (SRU) are used to build an automated system that can find anomalies in videos. The ResNet architecture takes high-level feature representations from the video frames that come in, while the SRU collects temporal features. The SRU has expressive recurrence and allows for highly parallelized implementation, which makes the video anomaly detection system more accurate. In the study, three models to detect anomalies are suggested as ResNet18 + SRU, ResNet34 + SRU, and ResNet50 + SRU, respectively. The suggested models are examined using the UCF-Crime dataset. This study made a clear distinction between normal and unusual actions, showing that CNN + SRU were able to put each unusual action in the right category. Using the UCF-Crime dataset, ResNet18 + SRU achieved 88.92% accuracy, ResNet34 + SRU achieved 89.34% accuracy, and ResNet50 + SRU achieved 91.24% accuracy. Furthermore, the proposed models demonstrated significantly higher performance accuracy and outscored the comparable deep learning models.