A memorization network model of normal environment for anomaly detection

The authors propose a three-layered network structure to detect abnormal objects in environments where surveillance cameras, security robots, and other image devices are employed for routine observations. By referring to the input patterns obtained from the environment, the network is structured to memorize the normal states of environments by constantly updating the connection weights in the network. As a result of learning, the network detects abnormal objects in input images. We conducted experiments in an office and in a corridor to verify the effectiveness of the proposed network for anomaly detection.