EgoDatasets: A Dataset-Centric Survey and Taxonomy for Egocentric Vision and Action Anticipation

Egocentric vision has become a central paradigm for studying human behaviour, interaction, and intent from a first-person perspective, supporting tasks that range from action recognition and temporal segmentation to future-action anticipation. Progress in this area depends strongly on public datasets, yet the current benchmark landscape remains fragmented across sensing configurations, annotation practices, application domains, temporal protocols, and evaluation settings. This survey presents a dataset-centric analysis of 96 publicly documented resources released between 2008 and 2026, comprising 79 core egocentric or multi-view datasets and 17 contextual exocentric datasets retained only when documented use in prospective human-action protocols could be established. We introduce a unified taxonomy spanning perspective, domain, sensing modality, annotation structure, and task support and use it to analyse temporal growth, benchmark concentration, cross-dimensional dependencies, anticipation protocols, label interoperability, modality documentation, and ego-exo alignment. The analysis shows that dataset availability has expanded substantially, but benchmark adoption remains highly concentrated, with established families dominating anticipation evaluation. Sensing coverage is strongly RGB-centric, prospective support is operationalised inconsistently across native, adapted, and post-hoc protocols, and several dataset attributes exhibit systematic co-occurrence rather than varying independently. Additionally, there is low cross-dataset compatibility: synchronized ego-exo resources are still uncommon, modality documentation is inconsistent, and action-label overlap is weak across benchmark families. This survey offers a shared reference for dataset selection, benchmark interpretation, cross-dataset comparison, and the design of future egocentric and action-anticipation datasets through a replicable taxonomy of the dataset landscape and a systematic assessment of its structural limitations.