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.

Evaluation Model of Operation State Based on Deep Learning for Smart Meter

The operation state detection of numerous smart meters is a significant problem caused by manual on-site testing. This paper addresses the problem of improving the malfunction detection efficiency of smart meters using deep learning and proposes a novel evaluation model of operation state for smart meter. This evaluation model adopts recurrent neural networks (RNN) to predict power consumption. According to the prediction residual between predicted power consumption and the observed power consumption, the malfunctioning smart meter is detected. The training efficiency for the prediction model is improved by using transfer learning (TL). This evaluation uses an accumulator algorithm and threshold setting with flexibility for abnormal detection. In the simulation experiment, the detection principle is demonstrated to improve efficient replacement and extend the average using time of smart meters. The effectiveness of the evaluation model was verified on the actual station dataset. It has accurately detected the operation state of smart meters.