Abnormal Detection in Big Data Video with an Improved Autoencoder

With the rapid growth of video surveillance data, there is an increasing demand for big data automatic anomaly detection of large-scale video data. The detection methods using reconstruction errors based on deep autoencoders have been widely discussed. However, sometimes the autoencoder could reconstruct the anomaly well and lead to missing detections. In order to solve this problem, this paper uses a memory module to enhance the autoencoder, which is called the memory-augmented autoencoder (Memory AE) method. Given the input, Memory AE first obtains the code from the encoder and then uses it as a query to retrieve the most relevant memory items for reconstruction. In the training phase, the memory content is updated and encouraged to represent prototype elements of normal data. In the test phase, the learned memory elements are fixed, and reconstruction is obtained from several selected memory records of normal data. So, the reconstruction will tend to be close to normal samples. Therefore, the reconstruction of abnormal errors will be strengthened for abnormal detection. The experimental results on two public video anomaly detection datasets, i.e., Avenue dataset and ShanghaiTech dataset, prove the effectiveness of the proposed method.