ADNet: Temporal Anomaly Detection in Surveillance Videos
This work addresses the problem of detecting anomalies in surveillance videos for security applications, but it is incremental as it builds on existing datasets and methods.
The paper tackles anomaly detection in surveillance videos by proposing ADNet, which uses temporal convolutions and a new AD Loss function to localize anomalies, achieving promising results on an extended UCF Crime dataset with the F1@k metric.
Anomaly detection in surveillance videos is an important research problem in computer vision. In this paper, we propose ADNet, an anomaly detection network, which utilizes temporal convolutions to localize anomalies in videos. The model works online by accepting consecutive windows consisting of fixed-number of video clips. Features extracted from video clips in a window are fed to ADNet, which allows to localize anomalies in videos effectively. We propose the AD Loss function to improve abnormal segment detection performance of ADNet. Additionally, we propose to use F1@k metric for temporal anomaly detection. F1@k is a better evaluation metric than AUC in terms of not penalizing minor shifts in temporal segments and punishing short false positive temporal segment predictions. Furthermore, we extend UCF Crime dataset by adding two more anomaly classes and providing temporal anomaly annotations for all classes. Finally, we thoroughly evaluate our model on the extended UCF Crime dataset. ADNet produces promising results with respect to F1@k metric. Dataset extensions and code will be publicly available upon publishing