Publications

Data Sets and Test Data
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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 happen to use this dataset, you can refer the following paper: J. Liu, J. Luo and M. Shah, Recognizing realistic actions from videos "in the wild", CVPR 2009, Miami, FL. ( For action 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 ).

Crowd Segmentation Dataset
This dataset contains videos of crowds and other high density moving objects. The videos are collected mainly from the BBC Motion Gallery and Getty Images website. The videos are shared only for the research purposes. Please consult the terms and conditions of use of these videos from the respective websites. The keyframes of the videos in the dataset are shown below. If you happen to use the dataset, please refer to the following paper:

Saad Ali and Mubarak Shah, A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2007.

Tracking in High Density Crowds Dataset
The Static Floor Field is aimed at capturing attractive and constant properties of the scene. These properties include preferred areas, such as dominant pathes often taken by the crowd as it moves through the scene, and preferred exit locations. Some video sequences and their corresponding static floor fields...

Please refer to this publication if you use this data set: Saad Ali and Mubarak Shah, Floor Fields for Tracking in High Density Crowd Scenes, The 10th European Conference on Computer Vision (EECV), 2008.

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