The Kinetics dataset is a large-scale, high-quality dataset for human action recognition in videos. The dataset consists of around 500,000 video clips covering 600 human action classes with at least 600 video clips for each action class. Each video clip lasts around 10 seconds and is labeled with a single action class. The videos are collected from YouTube.
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EgoProceL is a large-scale dataset for procedure learning. It consists of 62 hours of egocentric videos recorded by 130 subjects performing 16 tasks for procedure learning. EgoProceL contains videos and key-step annotations for multiple tasks from CMU-MMAC, EGTEA Gaze+, and individual tasks like toy-bike assembly, tent assembly, PC assembly, and PC disassembly. EgoProceL overcomes the limitations of third-person videos. As, using third-person videos makes the manipulated object small in appearance and often occluded by the actor, leading to significant errors. In contrast, we observe that videos obtained from first-person (egocentric) wearable cameras provide an unobstructed and clear view of the action.
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