Automatic Rush Generation with Application to Theatre Performances. (Généation Automatique de Prises de Vues Cinématographiques avec Applications aux Captations de Théâtre)

Professional quality videos of live staged performances are created by recording them from different appropriate viewpoints. These are then edited together to portray an eloquent story replete with the ability to draw out the intended emotion from the viewers. Creating such competent videos, involves the combination of multiple high quality cameras and skilled camera operators. We present a thesis to make even the low budget productions adept and pleasant by producing professional quality vidoes sans a fully and expensively equipped crew of cameramen. A high resolution static camera replaces the plural camera crew and their efficient camera movements are then simulated by virtually panning - tilting - zooming within the original recordings. We show that multiple virtual cameras can be simulated by choosing different trajectories of cropping windows inside the original recording. One of the key novelties of this work is an optimazation framework for computing the virtual camera trajectories using the information extracted from the original video based on computer vision techniques. The actors present on stage are considered as the most important elements of the scene. For the task of localizing and naming actors, we introduce generative models for learning view independent person and costume specific detectors from a set of labeled examples. We explain how to learn the models from a small number of labeled keyframes or video tracks, and how to detect novel appearances of the actors in a maximum likelihood framework. We demonstrate that such actor specific models can accurately localize actors despite changes in view point and occlusions, and significantly improve the detection recall rates over generic detectors. The dissertation then presents an offline algorithm for tracking objects and actors in long video sequences using these actor specific models. Detections are first performed to independently select candidate locations of the actor/object in each frame of the video. The candidate detections are then combined into smooth trajectories in an optimization step minimizing a cost function accounting for false detections and occlusions. Using the actor tracks, we propose a framework for automatically generating multiple clips suitable for video editing by simulating pan-tilt-zoom camera movements within the frame of a single static camera. Our method requires only minimal user input to define the subject matter of each sub-clip. The composition of each sub-clip is automatically computed in a novel L1-norm optimization framework. Our approach encodes several common cinematographic practices into a single convex cost function minimization problem, resulting in aesthetically-pleasing sub-clips which can easily be edited together using off-the-shelf multi-clip video editing software.