Review of visual AI applications in smart surveillance for crime detection and prevention: a survey

The application of Visual Artificial Intelligence (AI) in smart surveillance systems has signifi-cantly evolved, particularly in the domain of crime detection and prevention. This survey aims to com-prehensively review and analyze the implementation of visual AI technologies within a smart surveillance framework, focusing on their role in enhancing security measures for crime detection. Most frameworks found in the literature rely on the use of Convolutional Neural Network for image classification, however, areas of improvements are still open particularly the limited availability of datasets, high computational costs, and effective deployment of these types of applications. The survey investigates various facets, in-cluding but not limited to, object detection, facial recognition, anomaly detection, behavior analysis, and scene understanding within the context of surveillance applications. Furthermore, this survey aims to of-fer insights into the current state-of-the-art methodologies, recent innovations, and prospects in the realm of Visual AI-powered smart surveillance, providing a foundation for further research and development in this evolving field.