A MATCH-MOVING METHOD COMBINING AI AND SFM ALGORITHMS IN HISTORICAL FILM FOOTAGE
Keywords: Object Detection, Neural Networks, Camera Tracking, Photogrammetry, Cultural Heritage, Metric Quality Assessment
Abstract. Searching for suitable material for photogrammetry is a key part in the documentation of Cultural Heritage. Photogrammetry can be used to produce a metrically certified 3D model. Material contained in historical film footage archives is especially useful for documentation when the heritage has been lost. In this research an innovative match-moving method is proposed that aims to exploit Artificial Intelligence and SfM algorithms to identify the frames extracted from a film footage in which the lost monument appears and that are suitable to be processed with photogrammetry for its 3D reconstruction. First of all the identification and tracking of the heritage in the videos was performed training an object detection Neural Network. Then the frames detected were automatically extracted with the coordinates of the bounding boxes that contain the monument. The camera motions were identified by selecting only the shots taken from multiple points of view of the same scene and analysing the evolution of the bounding boxes position over time. A further check of the material was necessary to select only sequences and to eliminate single frames and images from different historic periods. After this process, only the correct frames were automatically selected and processed with photogrammetry and the quality of the obtained 3D model was assessed. The method experimented in this research represents a powerful tool in the field of Cultural Heritage because it makes the selection of suitable material for photogrammetry automatic. Moreover it offers important insights that could be extended to other sectors.