![]() Typically, there are between 23.97 and 30 frames per second of video. It usually includes hour, minute, second, and frame, in the format of hh:mm:ss:ff.įrame: A single image that is part of a video. These times are used for synchronization and to identify material in recorded media. Timecode: A sequence of numbers that contain the time information of media. Scene: Within the context of a Screenplay, a scene is a collection of events that happen within the same time frame and location. Screenplay: The written script for a movie, divided in scenes. Shooting ratio: The amount of raw footage shot divided by the final duration of the movie.Īspect ratio: The ratio of width to height. Raw footage: The media that has been shot or recorded but has not yet been edited. Adobe Premiere Pro is an example of non-linear editing (NLE) software. Non-linear editing: A form of editing that does not modify the original content, as opposed to classical editing of movies, which involved cutting and stitching physical film. To ensure this paper is self-contained, we present a brief description of the critical film terminology. Section 8 proposes potential improvements and future work. Section 7 describes the experimental results obtained from four film projects. Section 6 shows the interface provided by AutoTag within Adobe Premiere Pro. Section 5.2 describes the algorithm used for identification of cinematographic shot types. Section 5.1 describes the algorithm necessary to associate each media file to its corresponding scene. Section 5 summarizes the primary use case and workflow of AutoTag. Section 4 compares AutoTag to existing work. Section 3 establishes the technological tools and frameworks used in AutoTag. Section 2 introduces film terminology and tools, such as screenplay formatting, editing software, and linear timecode. The source code is freely available at the GitHub repository. Section 7.5 presents a case study.Ĭreate metadata for each media type and make it available within Adobe Premiere Pro as well as provide a search tool capable of filtering through our metadata more efficiently than Premiere. Our approach, as explained in Section 2.4, tags video footage with its corresponding screenplay portion either by matching the footage’s audio track or audio files to the linear timecode. Tag each video footage and audio file with its corresponding scene number and associated portions from a screenplay. Sections 7.2 and 7.3 present experiments. This process is described in Section 5.2. Unlike previous work, we use an unsupervised learning approach that relies on a ResNet SSD for facial recognition. This feature applies machine learning techniques to cinematic shot identification. Tag video footage with shot type (from close-up to long). The main contributions of AutoTag are to automate the following tasks: ![]()
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