Toward a System of Visual Classification, Analysis and Recognition of Performance-Based Moving Images in the Artistic Field

This paper proposes a research program focused on the design of a model for the recognition, analysis and classification of video art works and documentations based on their semiotic aspects and audiovisual content. Focusing on a corpus of art cinema, video art, and performance art, the theoretical framework involves bringing together semiotics, film studies, visual studies, and performance studies with the innovative technologies of computer vision and artificial intelligence. The aim is to analyze the performance aspect to interpret contextual references and cultural constructs recorded in artistic contexts, contributing to the classification and analysis of video art works with complex semiotic characteristics. Underlying the conceptual framework is the simultaneous use of a set of technologies, such as pose estimation, facial recognition, object recognition, motion analysis, audio analysis, and natural language processing, to improve recognition accuracy and create a large set of labeled audiovisual data. In addition, the authors propose a prototype application to explore the primary challenges of such a research project.