Detection and Localization of Abnormal Events for Smart Surveillance

Abstract

In this study, the methods of anomaly detection are proposed. Background substitution (BG) is used for extracting the motion and indicating the attention region's locations, which are employed. Then the regions are fed into the “Deep Convolutional Neural Network (DCNN)”. With the advantages of DCNN, for properly exploiting the spatiotemporal relationships, a network is developed for distinguishing anomalous and normal events. Besides this, the anomaly detection techniques are also described. The related databases are provided in this study. Many techniques for anomaly detection are discussed in this study with the help of the neural network. The different types of anomaly events are discussed here. All the data related to these anomaly events are discussed in the dataset. Different types of models related to the CNN model are also discussed in this study. And the anomaly techniques are also considered for discussion in this study.