Conv2D-LSTM-AE-GAN: Convolutional 2D LSTM Auto Encoder Generative Adversarial Network

Abstract

Surveillance video refers to video footage captured by cameras for the purpose of monitoring and recording activities in specific environments. These videos are commonly used for security purposes in places such as airports, shopping malls, streets, industrial facilities, hospitals, and other public or private spaces. The primary objective of surveillance video systems is to maintain safety, detect suspicious activities, and collect evidence for investigation. Anomaly detection in Surveillance video is an important and evolving field with applications across various industries. It involves analyzing video data to detect unusual or suspicious events, which could indicate threats, errors, or rare occurrences. While traditional methods have been useful, recent advancements in learning methods, particularly using 2D Convolutional Long Short Term Memory, Autoencoders, and Generative Adversarial Networks have made significant improvements in detecting complex anomalies. Our proposed system based on Autoencoder with Convolutional 2DLong Short Term Memory unit in Generative Adversarial Network. The model aims to learn the appropriate normal data distribution during training. Frames with a large variance in their regularity score are identified as anomalies based on this distribution. We have adopted depth-wise separable convolution with Conv2DLSTM unit in auto encoder to learn spatial and temporal features to reconstruct and differentiate generated frame with real frame in video sequence, and make the model lightweight and efficient. The entire system has been evaluated on many benchmark datasets using metrics like AUC and Equal Error Rate (EER) and shown to be reliable for complicated video anomaly identification.