Human Actions Data Sets



UCF-iPhone


Aerobic actions were recorded from subjects using the Inertial Measurement Unit (IMU) on an Apple iPhone 4 smartphone. The IMU includes a 3D accelerometer, gyroscope, and magnetometer. Each sample was taken at 60Hz, and manually trimmed to 500 samples (8.33s) to eliminate starting and stopping movements.


UCF-ARG


UCF-ARG (University of Central Florida-Aerial camera, Rooftop camera and Ground camera) Dataset is a Multiview Human Action dataset. UCF-ARG consists of 10 actions performed by 12 actors recorded from a ground camera, a rooftop camera at a height of 100 feet, and an aerial camera mounted onto the payload platform of a 13’ Kingfisher Aerostat helium balloon as illustrated in the figure.


UCF50
UCF50 is an action recognition dataset with 50 action categories, consisting of realistic videos taken from youtube. This dataset is an extension of YouTube Action dataset which has 11 action categories. Click here for UCF50 webpage


UCF Sports Action Dataset
This dataset consists of a set of actions collected from various sports which are typically featured on broadcast television channels such as the BBC and ESPN. The video sequences were obtained from a wide range of stock footage websites including BBC Motion gallery, and GettyImages.

This new dataset contains close to 200 video sequences at a resolution of 720x480. The collection represents a natural pool of actions featured in a wide range of scenes and viewpoints. By releasing the dataset we hope to encourage further research into this class of action recognition in unconstrained environments.

Actions in this dataset include:

Diving (16 videos)
Golf swinging (25 videos)
Kicking (25 videos)
Lifting (15 videos)
Horseback riding (14 videos)
Running (15 videos)
Skating (15 videos)
Swinging (35 videos)
Walking (22 videos)

If you use this data set, please refer to paper: Mikel D. Rodriguez, Javed Ahmed, and Mubarak Shah Action MACH: A Spatio-temporal Maximum Average Correlation Height Filter for Action Recognition.



UCF Aerial Action Dataset
This dataset features video sequences that were obtained using a R/C-controlled blimp equipped with an HD camera mounted on a gimbal.The collection represents a diverse pool of actions featured at different heights and aerial viewpoints. Multiple instances of each action were recorded at different flying altitudes which ranged from 400-450 feet and were performed by different actors.

The actions collected in this dataset include:

Walking
Running
Digging
Picking up an object
Kicking
Opening a car door
Closing a car door
Opening a car trunk
Closing a car trunk

All actions are annotated using the VIPER format.

UCF YouTube Action Dataset
1. It contains 11 action categories: basketball shooting, biking/cycling, diving, golf swinging, horse back riding, soccer juggling, swinging, tennis swinging, trampoline jumping, volleyball spiking, and walking with a dog.
2. This dataset is very challenging due to large variations in camera motion, object appearance and pose, object scale, viewpoint, cluttered background, illumination conditions, etc.
3. For each category, the videos are grouped into 25 groups with more than 4 action clips in it. The video clips in the same group share some common features, such as the same actor, similar background, similar viewpoint, and so on.
4. The videos are ms mpeg4 format. You need to install the right Codec (e.g. K-lite Codec Pack contains a cellection of Codecs) to access them.
5. If you use this dataset, please refer to following paper:
J. Liu, J. Luo and M. Shah, Recognizing realistic actions from videos "in the wild", CVPR 2009, Miami, FL.

Note: For actions biking and walking class, we select all the videos; for the rest of action classes, we only select the videos numbered from 01 to 04 from each group.