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We make available to the community a new dataset to supportaction-recognition research. This dataset is different from prior datasets inseveral key ways. It is significantly larger. It contains streaming video withlong segments containing multiple action occurrences that often overlap inspace and/or time. All actions were filmed in the same collection ofbackgrounds so that background gives little clue as to action class. We hadfive humans replicate the annotation of temporal extent of action occurrenceslabeled with their class and measured a surprisingly low level of intercoderagreement. A baseline experiment shows that recent state-of-the-art methodsperform poorly on this dataset. This suggests that this will be a challengingdataset to foster advances in action-recognition research. This manuscriptserves to describe the novel content and characteristics of the LCA dataset,present the design decisions made when filming the dataset, and document thenovel methods employed to annotate the dataset.

Collecting and Annotating the Large Continuous Action Dataset