RIT-18 is a novel compositional activity dataset collected by ACTION Lab at RIT containing 18 compositional activity classes. We collected video clips from 51 volleyball games on YouTube. With comprehensive annotations, RIT-18 is a large scale dataset for group activity understanding tasks such as group activity recognition, future activity anticipation, and rally-level winner prediction. The benchmark for these three tasks is provided.
Besides the aforementioned tasks, we believe RIT-18 dataset is potential for other less explored tasks, for example, temporal group activity localization, individual contribution evalution, and game-level winner prediction. Welcome to play!
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RIT-18
├── 0 # folder for video 0
├── 1
│ ├── clips # folder for clips
│ │ ├── 000.mp4
│ │ ├── ...
│ │ └── 029.mp4
│ ├── group.txt # activity label for all the clips of the video
│ └── info.txt # match description, video copyright, resolution and other format information
├── ...
├── 50
├── copyright.txt # all the links to the videos
└── annotation.pkl # all the annotations
We annotated 12035 frames picked from 1530 clips (51 videos * 30 clips / video) with 18 action labels.
| Activity Labels | No. of Instances | Activity Labels | No. of Instances |
|---|---|---|---|
| l-serve | 774 | r-serve | 756 |
| l-firstpass | 1395 | r-firstpass | 1395 |
| l-set | 1270 | r-set | 1266 |
| l-spike | 1012 | r-spike | 1014 |
| l-volley | 136 | r-volley | 143 |
| l-drop | 243 | r-drop | 235 |
| l-shot | 23 | r-shot | 23 |
| l-block | 393 | r-block | 427 |
| l-winpoint | 764 | r-winpoint | 766 |
group.txt
[clip-name].mp4
[a b] [a b] [left/right]
[key-frame-num] [start-frame-num] [end-frame-num] [key-time] [start-time] [end-time] [activity-label] [bounding box]
[key-frame-num] [start-frame-num] [end-frame-num] [key-time] [start-time] [end-time] [activity-label] [bounding box]
[key-frame-num] [start-frame-num] [end-frame-num] [key-time] [start-time] [end-time] [activity-label] [bounding box]
[key-frame-num] [start-frame-num] [end-frame-num] [key-time] [start-time] [end-time] [left/right winpoint]
e.g.
000.mp4
0 0 0 1 left
000000 000000 000026 0.000 0.000 0.520 r-serving 1073 330 1147 533
000052 000027 000101 1.040 0.540 2.010 l-passing 355 382 422 589
000124 000102 000164 2.480 2.030 3.285 l-setting 513 426 574 623
000182 000165 000201 3.640 3.305 4.020 l-spiking 472 381 541 601
000202 000202 000247 4.040 4.040 4.940 l-winpoint
info.txt
startTeam: [left/right]
winner: [left/right]
score: [a b]
1: [a b]
2: [a b]
3: [a b]
4: [a b]
link: [download link]
frameRate=[fps]
imWidth=[frame image width]
imHeight=[frame image height]
imExt=[frame image extension]
# 'left' and 'right' stand for the team position when the match or video begins
# 'a' is the score of 'left'
# 'b' is the score of 'right'
e.g.
startTeam: right
winner: right
score: 3 0
1: 25 22
2: 28 26
3: 26 24
link: https://www.youtube.com/watch?v=A1B2C3D4E
frameRate=25
imWidth=1920
imHeight=1080
imExt=.jpg
Winner Prediction baseline test.
cd winner
python scripts/test_volleyball_winner.py
Please cite our CVPR Workshop paper if use this dataset.
@InProceedings{RIT18_2020_CVPR_Workshops,
author = {Chen, Junwen and Hao, Haiting and Hong, Hanbin and Kong, Yu},
title = {RIT-18: A Novel Dataset for Compositional Group Activity Understanding},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshop},
month = {June},
year = {2020}
}