Dataset: UCF-CIL Action Dataset
Description:
UCF-CIL Action Dataset consists of 56 sequences of 8 actions:
- 4 of ballet fouettes
- 12 of ballet spin
- 6 of push-up exercise
- 8 for golf swing
- 4 of one-handed tennis backhand stroke
- 8 of two-handed tennis backhand stroke
- 4 of tennis forehand stroke
- 10 of tennis serve
Each action is performed by different subjects, and the videos are taken by different unknown cameras from various viewpoints collected over Internet. In addition, videos in the same group (action) may have different starting and ending times, thus may be only partially overlapped. Subjects also perform the same action in different ways and at different speeds.
We provide point tracking for each action. The dataset is arranged as follows: In the main directory, there are 8 different folders for each action. Each action folder has a number of folders for the different examples. Inside each such folder, the image files and the text files are provided. The image files are the different frames of the action and the corresponding text file contains the 11 points for that frame. The format of each text file is as follows: The first line gives the number of tracked points, which is always 11 in our case. This number is followed by 11 lines, each of which consist of 3 numbers. The first number is either 0 (denoting that the point is occluded) or 1 (point is visible). The next two numbers are the x and y coordinate of the point. The 11 points are always in this order: head, right shoulder, right elbow, right hand, left shoulder, left elbow, left hand, right knee, right foot, left knee, and left foot.
Publications:
- Yuping Shen and Hassan Foroosh, View Invariant Action Recognition from Point Triplets, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), to appear, 2009. (PDF)
- Yuping Shen and Hassan Foroosh, View Invariant Action Recognition Using Fundamental Ratios, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. (PDF)
- Yuping Shen and Hassan Foroosh, View Invariant Recognition of Body Pose from Space-Time Templates, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. (PDF)
Download:
UCF-CIL Action Dataset (Image sequences and labeled body joints)