Utilizing Cutting-Edge Machine Vision Techniques for Swift Anomaly Detection in Suboptimal Surveillance Environments

In the realm of surveillance and anomaly detection, the proposed Adaptive Multimodal Anomaly Detection (AMAD) method stands as an innovative and potent approach. The objective of this method is to detect anomalies swiftly and accurately in suboptimal surveillance environments by amalgamating state-of-the-art machine vision techniques with multimodal data fusion. The core idea revolves around leveraging advanced deep learning architectures and statistical models, thereby enhancing the performance of anomaly detection systems. The AMAD method initiates with Algorithm 1: Multimodal Data Fusion, a process that involves integrating multimodal data streams, such as visual, thermal, and infrared information, into a unified feature space. This integration is accomplished using a weighted sum approach, allowing varying importance levels to be assigned to each modality based on weights
$(\alpha, \beta, \gamma)$
. This fusion strategy proves pivotal in capturing a holistic representation of the environment. Convolutional Neural Network (CNN) for Feature Learning follows, where a CNN architecture is employed to learn robust and discriminative features from the fused data. This study showcases that AMAD, with its multimodal integration, deep learning-based feature extraction, and efficient scoring mechanism, triumphs over traditional anomaly detection techniques. It significantly contributes to enhanced anomaly detection capabilities, emphasizing its potential for bolstering public safety and security in dynamic and challenging technological landscapes.