Anomaly detection in video sequences: A benchmark and computational model

Abstract

Abstract Anomaly detection has attracted considerable search attention. However, existing anomaly detection databases encounter two major problems. Firstly, they are limited in scale. Secondly, training sets contain only video‐level labels indicating the existence of an abnormal event during the full video while lacking annotations of precise time durations. To tackle these problems, we contribute a new Large‐scale Anomaly Detection (LAD) database as the benchmark for anomaly detection in video sequences, which is featured in two aspects. 1) It contains 2000 video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence etc. with large scene varieties, making it the largest anomaly analysis database to date. 2) It provides the annotation data, including video‐level labels (abnormal/normal video, anomaly type) and frame‐level labels (abnormal/normal video frame) to facilitate anomaly detection. Leveraging the above benefits from the LAD database, we further formulate anomaly detection as a fully supervised learning problem and propose a multi‐task deep neural network to solve it. We firstly obtain the local spatiotemporal contextual feature by using an Inflated 3D convolutional (I3D) network. Then we construct a recurrent convolutional neural network fed the local spatiotemporal contextual feature to extract the spatiotemporal contextual feature. With the global spatiotemporal contextual feature, the anomaly type and score can be computed simultaneously by a multi‐task neural network. Experimental results show that the proposed method outperforms the state‐of‐the‐art anomaly detection methods on our database and other public databases of anomaly detection. Supplementary materials are available at http://sim.jxufe.cn/JDMKL/ymfang/anomaly‐detection.html.

Keywords

• Image recognition
• Computer vision and image processing techniques
• Video signal processing
• Data handling techniques
• Supervised learning
• Neural nets