Abstract
The position and orientation of the camera in relation to the subject(s) in a movie scene, namely camera "level" and camera "angle", are essential features in the film-making process due to their influence on the viewer's perception of the scene. We provide a database containing camera feature annotations on camera angle and camera level, for about 25,000 image frames. Frames are sampled from a wide range of movies, freely available images, and shots from cinematographic websites, and are annotated on the following five categories - Overhead, High, Neutral, Low, and Dutch - for what concerns camera angle, and on six different classes of camera level: Aerial, Eye, Shoulder, Hip, Knee, and Ground level. This dataset is an extension of the Cinescale dataset [1], which contains movie frames and related annotations regarding shot scale. The CineScale2 database enables AI-driven interpretation of shot scale data and opens to a large set of research activities related to the automatic visual analysis of cinematic material, such as movie stylistic analysis, video recommendation, and media psychology. To these purposes, we also provide the model and the code for building a Convolutional Neural Network (CNN) architecture for automated camera feature recognition. All the material is provided on the the project website; video frames can be also provided upon requests to authors, for research purposes under fair use.