An anomaly detection method based on feature regular constraints

Abstract:

Anomaly detection is a classic problem in computer vision. It is difficult to capture the anomalies in the real scene and is difficult to obtain the labels as well, an endtoend anomaly detection model trained only with normal samples is proposed. First, the input image is encoded by an automatic encoder to obtain its lowdimensional features, and then an autoregressive probability density estimator is used to constrain the probability distribution of low dimensional features. The decoder restores it to the original input size. Finally, a classifier determines the authenticity of the generated picture. A jumper connection is used between the codecs to maximize the memory of the model for normal samples. In this paper, the experiments were conducted on the CIFAR10 and UCSD Ped2 datasets. The results showed that the average area under the curve (AUC) of the 10 categories of CIFAR10 reached 73.5%, and the area under the average curve (AUC) of UCSDPed2 reached 95.7%. This model can effectively improve the effect of anomaly detection.