Improving Multi-Camera View Recommendation with Temporal and Camera Embedding

Multi-camera systems are essential in movies, live broadcasts, and other media. The selection of the appropriate camera for every moment has a decisive impact on production quality and audience preferences. Learning-based multi-camera view recommendation frameworks have been explored to assist professionals in decision making. This work explores how two standard cinematography practices could be incorporated into the learning pipeline: (1) not staying on the same camera for too long and (2) introducing a scene from a wider shot and gradually progressing to narrower ones. In these regards, we incorporate (1) the duration of the displaying camera and (2) camera identity as temporal and camera embedding in a transformer architecture, thereby implicitly guiding the model to learn the two practices from professional-labeled data. Experiments show that the proposed framework outperforms the baseline by 14.68% in six-way classification accuracy. Ablation studies on different approaches to embedding the temporal and camera information further verify the efficacy of the framework.