Video anomaly detection for industrial equipment that repeats multiple patterns

Abstract

The purpose of this research is video anomaly detection for industrial devices that perform repetitive motions in multiple patterns. Deep learning technology has been developing rapidly in recent years and has attracted much attention for industrial applications. Unsupervised learning, in which only normal conditions are learned, is effective because it is easy to use even for objects for which it is difficult to define abnormal conditions in advance, and is beginning to be widely used in image inspection and other applications. On the other hand, there are timing anomalies (anomalies that can be detected only by considering the regularity of temporal changes in motion) that are difficult to detect using only still images, and unsupervised learning is required to detect such anomalies. When detecting video abnormality, it is relatively easy to detect timing abnormality if the device always repeats the same action. This is the reason why we have developed this method. In this presentation, we will discuss a highly accurate timing error detection method for industrial equipment that performs repetitive operations with multiple patterns, and report the results of a method that combines AE and LSTM and a method that uses PredNet to obtain high accuracy.