Anomaly Detection on Public Streets Using Spatial Features and a Bidirectional Sequential Classifier

The anomaly detection problem consists in identifying the events that do not conform to an expected behavior pattern. In law enforcement and security, detection of anomalous events has application in the identification of suspicious behaviors. This paper addresses such problem in public areas by monitoring surveillance videos. Our approach involves a convolutional neural network for spatial features extraction, followed by a time series classifier with a one-dimensional convolutional layer and an ensemble of stacked bidirectional recurrent networks. The proposed methodology selects a pre-trained convolutional architecture for the spatial feature and applies transfer learning to specialize this architecture in anomaly detection in surveillance videos. We performed the experiments on the UCSD Anomaly Detection Dataset and the CUHK Avenue Dataset for Abnormal Event Detection to compare our approach with other works. Our evaluation protocol uses the Area Under the Receiver Operating Characteristic Curve—AUC, the Equal Error Rate—EER, and the Area Under the Precision vs. Recall Curve—AUPRC. During the experiments, the model obtained AUC above $$92\%$$ and EER below $$15\%$$ , which are compatible with the current literature.