Exploring multi-camera views from user-generated sports videos - Pessoa, Larissa; Alencar, Elton; Costa, Fernanda; Souza, Guilherme; Freitas, Rosiane de (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2024.244721, 2024)
The proliferation of mobile devices with video recording capabilities has revolutionized audiovisual content creation, sharing, and consumption, turning user-generated video (UGV) platforms into major data sources. Despite this growth, there is a notable gap in publicly available datasets featuring multiangle recordings of sports events captured with various mobile cameras. This paper introduces the MUVY Dataset, which offers a diverse collection of sports videos from multiple perspectives, unrestricted by video size. The dataset addresses common challenges in user-generated videos, such as shaking, occlusions, blurring, and abrupt movements. Each video is accompanied by metadata that include camera identification, YouTube URLs, extracted frames, and object annotations. Covering sports like soccer, American football, artistic gymnastics, athletics, basketball, tennis, and cricket, the MUVY Dataset facilitates advancements in video understanding and viewpoint selection. Initial experiments in camera pose estimation demonstrate the dataset’s potential for training models in this domain. Additionally, it supports the selection of the closest viewpoint based on object detection and the relative area occupied by detected objects. Overall, the MUVY Dataset aims to advance multi-camera video analysis and related research areas.