A study of the effect of deep cascade network on anomaly detection using optical flow as a high-level additional signal

Video anomaly detection by reconstruction is a challenging task. One of its challenges is related to the volume of input data frames needed to be processed to detect anomalies. The challenge usually manifests itself as increased training and especially testing time. The proposed architecture boosts performance while maintaining the same test time as our previously introduced AnoDetNet architecture. The proposed architecture is a cascaded framework that is a succession of reconstruction and an auxiliary network. Upon training, the auxiliary network acts as guidance through the use of combined loss. The combined training of the networks results in a performance increase compared with the reconstruction case alone. Considering that the auxiliary network's results are not used in the test phase, the overall anomaly detection test time does not change compared with the non-cascaded architecture. Two possible auxiliary networks, namely edge detection and optical flow estimation are studied. The proposed architecture results in state-of-the-art results on the Ped2 and Avenue datasets.