A cutting-edge video anomaly detection method using image quality assessment and attention mechanism-based deep learning

Abstract

Video anomaly detection is a very important research topic, especially in the field of automatic driving, and timely acquisition of road conditions ahead is very important for safe driving. Past methods used convolution and other operations, resulting in severe delays or low detection accuracy. This paper introduces a cutting-edge video anomaly detection method that combines the advantages of image quality assessment and attention mechanisms while using image signal processing techniques to enhance the data. Our approach first uses image quality metrics to evaluate visual fidelity anomalies for each video frame, providing a computationally simple and effective method for initial anomaly recognition. Then, an attention mechanism is integrated into two deep learning models including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. This integration enables the system to dynamically focus on significant regions in the video. This capability is unmatched by traditional analysis methods. The dual-structure system shows higher sensitivity to various anomalies. In addition, the system is also equipped with image signals, which makes the data feature extraction more precise. Through thorough testing of the benchmark dataset, the proposed approach has been shown to significantly outperform current state-of-the-art models. It especially excels in challenging monitoring scenarios. For example, in the UCSD dataset, we achieved an area under curve (AUC) value of 99.9%, which exceeds most current methods.

Keywords

• Video anomaly detection
• Image quality assessment
• Attention mechanism
• Deep learning
• Robustness