CLOUD-SMART SURVEILLANCE: ENHANCING ANOMALY DETECTION IN VIDEO STREAMS WITH DF-CONVLSTM-BASED VAE-GAN

Abstract

Anomaly detection in computer vision is crucial, and manual identification of irregularities in videos is resource-intensive. Autonomous systems are essential for efficiently analysing and detecting anomalies in diverse video datasets. Video surveillance relies heavily on anomaly detection for monitoring equipment states through time-series data. Presently, deep learning methods, particularly those based on Generative Adversarial Networks (GAN), have gained prominence in time-series anomaly detection. This paper proposes a novel solution: the double-flow convolutional Long Short-Term Memory (DF-ConvLSTM) - based Variational Autoencoder- Generative Adversarial Network (VAE-GAN) method. By co-training the encoder, generator, and discriminator, this approach leverages the encoder's mapping skills and the discriminator's discrimination capabilities simultaneously. The proposed strategy is compared with LSTM-VAE, LSTM-VAE-Attention, and VAE. The proposed method is evaluated using metrics for recall, accuracy, precision, and F1 score. With classification accuracies of 91% on the University of Central Florida (UCF) crime dataset, the experimental results outperformed alternative techniques. Furthermore, the analysis of the ROC curve revealed that the suggested method performed better than the others, as evidenced by its higher ROC (Receiver Operating Characteristic) values. Experimental results demonstrate the proposed method's ability to rapidly and accurately detect anomalies in surveillance videos, ensuring efficient and reliable anomaly detection. Experimental results show the method's rapid, accurate anomaly detection in surveillance videos, ensuring efficiency and reliability. However, challenges include high computational costs, affecting the practicality of implementation for real-time anomaly detection.