  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
