Top 5 Pre-Labeled Video Datasets for Action Recognition

Are you looking for pre-labeled video datasets for action recognition? Look no further! In this article, we will introduce you to the top 5 pre-labeled video datasets for action recognition. These datasets are perfect for training and testing your machine learning models for action recognition.

1. UCF101

UCF101 is a widely used pre-labeled video dataset for action recognition. It contains 13,320 videos of 101 action categories, such as basketball dunk, horse race, and playing guitar. The videos are collected from YouTube and have a duration of 10 seconds on average. The dataset is split into three subsets: training, validation, and testing. The training set contains 9,537 videos, the validation set contains 3,783 videos, and the testing set contains 3,823 videos. The dataset also provides human-labeled action annotations for each video.

UCF101 is a great dataset for action recognition because it covers a wide range of action categories and has a large number of videos. It is also widely used in research papers and competitions, which makes it easy to compare your results with others.

2. HMDB51

HMDB51 is another popular pre-labeled video dataset for action recognition. It contains 6,766 videos of 51 action categories, such as brushing teeth, jumping jacks, and playing piano. The videos are collected from various sources, such as movies and YouTube, and have a duration of 3 to 10 seconds. The dataset is split into three subsets: training, validation, and testing. The training set contains 3,529 videos, the validation set contains 1,358 videos, and the testing set contains 1,879 videos. The dataset also provides human-labeled action annotations for each video.

HMDB51 is a great dataset for action recognition because it covers a wide range of action categories and has a moderate number of videos. It is also widely used in research papers and competitions, which makes it easy to compare your results with others.

3. Kinetics

Kinetics is a large-scale pre-labeled video dataset for action recognition. It contains 300,000 videos of 400 action categories, such as blowing out candles, playing accordion, and washing hair. The videos are collected from YouTube and have a duration of 10 seconds on average. The dataset is split into three subsets: training, validation, and testing. The training set contains 240,000 videos, the validation set contains 20,000 videos, and the testing set contains 40,000 videos. The dataset also provides human-labeled action annotations for each video.

Kinetics is a great dataset for action recognition because it covers a wide range of action categories and has a huge number of videos. It is also challenging because some action categories have very few videos, which makes it difficult to train a machine learning model. However, Kinetics is a great dataset for researchers who want to push the limits of action recognition.

4. Something-Something

Something-Something is a unique pre-labeled video dataset for action recognition. It contains 220,847 videos of 174 action categories, such as blowing a kiss, high-fiving, and playing with hair. The videos are collected from crowd-sourced videos and have a duration of 2 to 6 seconds. The dataset is split into three subsets: training, validation, and testing. The training set contains 168,913 videos, the validation set contains 24,777 videos, and the testing set contains 27,157 videos. The dataset also provides human-labeled action annotations for each video.

Something-Something is a great dataset for action recognition because it covers a wide range of action categories and has a large number of videos. It is also unique because the videos are collected from crowd-sourced videos, which makes it more diverse and challenging. Something-Something is a great dataset for researchers who want to explore the limits of action recognition.

5. Breakfast

Breakfast is a pre-labeled video dataset for action recognition in the context of meal preparation. It contains 1,716 videos of 10 action categories, such as pouring milk, spreading butter, and frying egg. The videos are collected from 52 participants preparing breakfast and have a duration of 2 to 5 minutes. The dataset is split into three subsets: training, validation, and testing. The training set contains 1,184 videos, the validation set contains 250 videos, and the testing set contains 282 videos. The dataset also provides human-labeled action annotations for each video.

Breakfast is a great dataset for action recognition because it is unique and challenging. It focuses on a specific context, which makes it more realistic and applicable to real-world scenarios. Breakfast is a great dataset for researchers who want to explore the limits of action recognition in the context of meal preparation.

Conclusion

In conclusion, pre-labeled video datasets are essential for training and testing machine learning models for action recognition. The top 5 pre-labeled video datasets for action recognition are UCF101, HMDB51, Kinetics, Something-Something, and Breakfast. These datasets cover a wide range of action categories and have different sizes and challenges. Choosing the right dataset depends on your research goals and resources. We hope this article helps you find the right pre-labeled video dataset for your machine learning project.

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