Abstract
On the existing benchmark datasets, THUMOS14 and ActivityNet, temporal action localization techniques have achieved great success. However, there are still existing some problems, such as the source of the action is too single, there are only sports categories in THUMOS14, coarse instances with uncertain boundaries in ActivityNet and HACS Segments interfering with proposal generation and behavior prediction. To take temporal action localization to a new level, we develop FineAction, a new large-scale fined video dataset collected from existing video datasets and web videos. Overall, this dataset contains 139K fined action instances densely annotated in almost 17K untrimmed videos spanning 106 action categories. FineAction has a more fined definition of action categories and high-quality annotations to reduce the boundary uncertainty compared to the existing action localization datasets. We systematically investigate representative methods of temporal action localization on our dataset and obtain some interesting findings with further analysis. Experimental results reveal that our FineAction brings new challenges for action localization on fined and multi-label instances with shorter duration. This dataset will be public in the future and we hope our FineAction could advance research towards temporal action localization. Our dataset website is at this https URL.
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URL
https://arxiv.org/abs/2105.11107
