A multi-memory-augmented network with a curvy metric method for video anomaly detection.

Abstract

Anomaly detection task in video mainly refers to identifying anomalous events that do not conform to the learned normal patterns in the inferring phase. However, the Euclidean metric used in the learning and inferring phase by the most of the existing methods, which cannot measure the difference between the different high-dimensional data reasonably, because the Euclidean distance between the different high-dimensional data will gradually become the same as the dimension increases. In this paper, we propose a Multi-Memory-Augmented dual-flow network with a new curvy metric method, to remove this shortcoming of Euclidean metric. To the best of our knowledge, this is the first work to detect abnormal events using this novel curvy metric. A large number of comparative experiments show that this novel curvy metric can be inserted in any neural network based on the Euclidean metric due to its independence and the migration experiment results. In addition, the powerful representation capacity of deep network allows to take abnormal frames as normal, we employ several memory units to the dual-flow network that considers the diversity of normal patterns explicitly, while lessening the representation capacity of dual-flow network. Our model is easy to be trained and robust to be applied. Extensive experiments on five publicly available datasets verify the validity of our method, which reflect in the robustness to the normal events diversity as well as the sensitivity to abnormal events.