Real-Time Anomaly Detection in Video Surveillance: A Mathematical Modeling and Nonlinear Analysis Perspective with MobileNet and Bi-LSTM

Abstract

For the main purpose of security and safety, video surveillance systems are everywhere and their need for presence is at the top. The prominent application of a video monitoring system is to track and monitor the live footage and recognize any unusual activity or behavior. For the prevention of any unwanted event or dangerous activity, it is mandatory to get early detection from video surveillance. The previous architectures of traditional systems were primarily based on manual monitoring anomaly detection which had increased the false negative rate of anomaly detection. In this paper, we proposed a customized framework that leverages MobileNet and Bi-LSTM for the accurate detection of anomalies in real-time with unsupervised learning techniques. Due to the spatial-temporal feature pattern and multi-modal functionality of MobileNet, it helps in increasing accuracy compared to previous systems. MobileNet uses depth-wise and point-wise convolution than the normal convolution which improves the performance metrics. This paper has illustrated a brief study of benchmark datasets such as UCF dataset including their performance in anomaly detection compared with traditional systems and existing deep learning approaches. The proposed model has given an accuracy of 95.33% compared to the latest year 2023 model which gave an accuracy of 90.80% on the same dataset.