Anomaly Detection in Surveillance Videos Using Regression With Recurrent Neural Networks

Abstract

Security cameras are widely used to detect and prevent crimes, but the number of surveillance videos has increased due to this prevalence. By processing these videos with the help of a suitable machine learning algorithm, unfavorable events can be brought to the attention of expert to manually monitor. Since these unfavorable events are of various types and few in number, this problem can be addressed in the anomaly detection structure. In this study, an anomaly detection algorithm has been developed using the UCF-Crime dataset consisting of 1900 surveillance videos of various lengths. First of all, features were extracted from these videos with the help of a pre-trained artificial neural network (ANN), the size of these features was reduced with another ANN, and the anomaly detection was performed using two different recurrent neural networks, one based on classification and the other based on future feature estimation by regression. Area under receiver operating characteristic (ROC) curve (AUC) was used as the evaluation criterion. At video level, regression method gives a better performance with 88.71% AUC than the classification method which only gives 85.82% AUC, while at video frame level, both methods perform similarly with 73.64% and 73.71%, but there are true positive rate ranges where they perform better than each other.