UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
The UCF101 dataset contains over 13,000 video clips of 101 different human actions, totaling 27 hours of footage captured in the real world with camera shake and busy backgrounds.
Why it matters
The UCF101 dataset provides a realistic and challenging benchmark for advancing computer vision and human action recognition capabilities.
Key Points
- 1UCF101 dataset contains 101 human action classes and over 13,000 video clips
- 2The videos are captured in the wild, not in a studio, making them more challenging for computer vision models
- 3Simple computer methods can only correctly identify the actions about 44% of the time
- 4The dataset provides a challenging playground for researchers building systems to understand human actions
Details
The UCF101 dataset is a collection of over 13,000 short video clips showing 101 different human actions, such as running, jumping, cooking, and dancing. The videos are captured in the real world, with camera shake and busy backgrounds, rather than in a controlled studio setting. This makes the dataset more representative of everyday footage, but also more challenging for computer vision models to analyze. Simple methods can only correctly identify the actions about 44% of the time, indicating the difficulty of the task. The dataset is intended to serve as a challenging playground for researchers building systems to watch and understand human actions, helping them identify weaknesses and test new ideas.
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