The CASTLE 2024 Dataset: Advancing the Art of Multimodal Understanding
This dataset provides a comprehensive, uncensored resource for researchers in multimodal understanding, though it is incremental as it builds on existing dataset efforts.
The paper introduces the CASTLE 2024 dataset, a multimodal collection with over 600 hours of UHD video from 15 time-aligned ego- and exo-centric sources, addressing the limitation of single-perspective datasets in egocentric video research.
Egocentric video has seen increased interest in recent years, as it is used in a range of areas. However, most existing datasets are limited to a single perspective. In this paper, we present the CASTLE 2024 dataset, a multimodal collection containing ego- and exo-centric (i.e., first- and third-person perspective) video and audio from 15 time-aligned sources, as well as other sensor streams and auxiliary data. The dataset was recorded by volunteer participants over four days in a fixed location and includes the point of view of 10 participants, with an additional 5 fixed cameras providing an exocentric perspective. The entire dataset contains over 600 hours of UHD video recorded at 50 frames per second. In contrast to other datasets, CASTLE 2024 does not contain any partial censoring, such as blurred faces or distorted audio. The dataset is available via https://castle-dataset.github.io/.