UCF101: A Dataset of 101 Human Actions
Classes From Videos in The Wild
Khurram Soomro, Amir Roshan Zamir and Mubarak Shah
CRCV-TR-12-01
November 2012
Keywords: Action Dataset, UCF101, UCF50, Action Recognition
Center for Research in Computer Vision
University of Central Florida
4000 Central Florida Blvd.
Orlando, FL 32816-2365 USA
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
Khurram Soomro, Amir Roshan Zamir and Mubarak Shah
Center for Research in Computer Vision, Orlando, FL 32816, USA
{ksoomro, aroshan, shah}@cs.ucf.edu
http://crcv.ucf.edu/data/UCF101.php
Abstract
We introduce UCF101 which is currently the largest
dataset of human actions. It consists of 101 action classes,
over 13k clips and 27 hours of video data. The database
consists of realistic user-uploaded videos containing camera motion and cluttered background. Additionally, we provide baseline action recognition results on this new dataset
using standard bag of words approach with overall performance of 43.9%. To the best of our knowledge, UCF101
is currently the most challenging dataset of actions due to
its large number of classes, large number of clips and also
unconstrained nature of such clips.
Apply Eye Makeup
Playing Dhol
Baby Crawling
Haircut
Sky Diving
Surfing
Rafting
Cricket Shot
Shaving Beard
1. Introduction
Figure 1. Sample frames for 6 action classes of UCF101.
The majority of existing action recognition datasets suffer from two disadvantages: 1) The number of their classes
is typically very low compared to the richness of performed
actions by humans in reality, e.g. KTH [11], Weizmann [3],
UCF Sports [10], IXMAS [12] datasets includes only 6, 9,
9, 11 classes respectively. 2) The videos are recorded in unrealistically controlled environments. For instance, KTH,
Weizmann, IXMAS are staged by actors; HOHA [7] and
UCF Sports are composed of movie clips captured by professional filming crew. Recently, web videos have been
used in order to utilize unconstrained user-uploaded data to
alleviate the second issue [6, 8, 9, 5]. However, the first disadvantage remains unresolved as the largest existing dataset
does not include more than 51 actions while several works
showed that the number of classes play a crucial role in evaluating an action recognition method [4, 9]. Therefore, we
have compiled a new dataset with 101 actions and 13320
clips which is nearly twice bigger than the largest existing
dataset in terms of number of actions and clips. (HMDB51
[5] and UCF50 [9] are the currently the largest ones with
6766 clips of 51 actions and 6681 clips of 50 actions respectively.)
The dataset is composed of web videos which are
recorded in unconstrained environments and typically in-
clude camera motion, various lighting conditions, partial
occlusion, low quality frames, etc. Fig. 1 shows sample
frames of 6 action classes from UCF101.
2. Dataset Details
Action Classes: UCF101 includes total number of
101 action classes which we have divided into five types:
Human-Object Interaction, Body-Motion Only, HumanHuman Interaction, Playing Musical Instruments, Sports.
UCF101 is an extension of UCF50 which included the
following 50 action classes: {Baseball Pitch, Basketball
Shooting, Bench Press, Biking, Billiards Shot, Breaststroke,
Clean and Jerk, Diving, Drumming, Fencing, Golf Swing,
High Jump, Horse Race, Horse Riding, Hula Hoop, Javelin
Throw,, Juggling Balls, Jumping Jack, Jump Rope, Kayaking, Lunges, Military Parade, Mixing Batter, Nun chucks,
Pizza Tossing, Playing Guitar, Playing Piano, Playing
Tabla, Playing Violin, Pole Vault, Pommel Horse, Pull Ups,
Punch, Push Ups, Rock Climbing Indoor, Rope Climbing,
Rowing, Salsa Spins, Skate Boarding, Skiing, Skijet, Soccer Juggling, Swing, TaiChi, Tennis Swing, Throw Discus,
1
Apply Eye Makeup
Apply Lipstick Blow Dry Hair Brushing Teeth Cutting In Kitchen
Knitting
Mixing Batter
Mopping Floor
Writing On Board
Yo Yo
Pull ups
Push ups
Band Marching
Haircut
Playing Flute
Playing Guitar
Playing Piano
Basketball
Basketball Dunk
Clean and Jerk
Nun Chucks
Baby Crawling Blowing Candles
Pizza Tossing
Hammering
Hula Hoop
Juggling Balls
Shaving Beard Skate Boarding Soccer Juggling
Body Weight Squats Handstand Pushups Handstand Walking
Jumping Jack
Jump Rope
Typing
Lunges
Swing
Tai Chi
Salsa Spin
Drumming
Playing Cello
Playing Daf
Playing Dhol
Playing Sitar
Playing Tabla
Playing Violin
Archery
Balance Beam
Baseball Pitch
Bench Press
Biking
Billiard
Bowling
Boxing-Punching Bag Boxing-Speed Bag
Breaststroke
Cliff Diving
Cricket Bowling
Cricket Shot
Diving
Fencing
Field Hockey Penalty Floor Gymnastics
Frisbee Catch
Front Crawl
Golf Swing
Hammer Throw
High Jump
Horse Race
Horse Riding
Ice Dancing
Javelin Throw
Kayaking
Long Jump
Parallel Bars
Pole Vault
Pommel Horse
Punch
Rafting
Rowing
Shotput
Skiing
Jetski
Sky Diving
Soccer Penalty
Still Rings
Sumo Wrestling
Surfing
Table Tennis Shot
Tennis Swing
Throw Discus
Uneven Bars
Volleyball Spiking
Rock Climbing Indoor
Rope Climbing
Head Massage Military Parade
Trampoline Jumping Walking with a Dog
Wall Pushups
Figure 2. 101 actions included in UCF101 shown with one sample frame. The color of frame borders specifies to which action type they
belong: Human-Object Interaction, Body-Motion Only, Human-Human Interaction, Playing Musical Instruments, Sports.
Trampoline Jumping, Volleyball Spiking, Walking with a
dog, Yo Yo}. The color class labels specify which predefined action type they belong to.
The following 51 new classes are introduced in UCF101:
{Apply Eye Makeup, Apply Lipstick, Archery, Baby Crawling, Balance Beam, Band Marching, Basketball Dunk, Blow
Drying Hair, Blowing Candles, Body Weight Squats, Bowl-
ing,Boxing-Punching Bag, Boxing-Speed Bag, Brushing
Teeth, Cliff Diving, Cricket Bowling, Cricket Shot, Cutting In Kitchen, Field Hockey Penalty, Floor Gymnastics,
Frisbee Catch, Front Crawl, Hair cut, Hammering, Hammer Throw, Handstand Pushups, Handstand Walking, Head
Massage, Ice Dancing, Knitting, Long Jump, Mopping
Floor, Parallel Bars, Playing Cello, Playing Daf, Playing
180
Number of Clips
150
120
90
60
30
ApplyEyeMakeup
ApplyLipstick
Archery
BabyCrawling
BalanceBeam
BandMarching
BaseballPitch
Basketball
BasketballDunk
BenchPress
Biking
Billiards
BlowDryHair
BlowingCandles
BodyWeightSquats
Bowling
BoxingPunchingBag
BoxingSpeedBag
BreastStroke
BrushingTeeth
CleanAndJerk
CliffDiving
CricketBowling
CricketShot
CuttingInKitchen
Diving
Drumming
Fencing
FieldHockeyPenalty
FloorGymnastics
FrisbeeCatch
FrontCrawl
GolfSwing
Haircut
Hammering
HammerThrow
HandstandPushups
HandstandWalking
HeadMassage
HighJump
HorseRace
HorseRiding
HulaHoop
IceDancing
JavelinThrow
JugglingBalls
JumpingJack
JumpRope
Kayaking
Knitting
0
180
> 10.0 Sec
5.0 - 10.0 Sec
2.0 - 5.0 Sec
0.0 - 2.0 Sec
Number of Clips
150
120
90
60
0
LongJump
Lunges
MilitaryParade
Mixing
MoppingFloor
Nunchucks
ParallelBars
PizzaTossing
PlayingCello
PlayingDaf
PlayingDhol
PlayingFlute
PlayingGuitar
PlayingPiano
PlayingSitar
PlayingTabla
PlayingViolin
PoleVault
PommelHorse
PullUps
Punch
PushUps
Rafting
RockClimbi…
RopeClimbing
Rowing
SalsaSpin
ShavingBeard
Shotput
SkateBoarding
Skiing
Skijet
SkyDiving
SoccerJuggling
SoccerPenalty
StillRings
SumoWrestling
Surfing
Swing
TableTennis…
TaiChi
TennisSwing
ThrowDiscus
TrampolineJ…
Typing
UnevenBars
VolleyballSp…
WalkingWit…
WallPushups
WritingOnB…
YoYo
30
Figure 3. Number of clips per action class. The distribution of clip durations is illustrated by the colors.
Dhol, Playing Flute, Playing Sitar, Rafting, Shaving Beard,
Shot put, Sky Diving, Soccer Penalty, Still Rings, Sumo
Wrestling, Surfing, Table Tennis Shot, Typing, Uneven Bars,
Wall Pushups, Writing On Board}. Fig. 2 shows a sample
frame for each action class of UCF101.
Clip Groups: The clips of one action class are divided
into 25 groups which contain 4-7 clips each. The clips in
one group share some common features, such as the background or actors.
The bar chart of Fig. 3 shows the number of clips in
each class. The colors on each bar illustrate the durations
of different clips included in that class. The chart shown in
Fig. 4 illustrates the average clip length (green) and total
duration of clips (blue) for each action class.
The videos are downloaded from YouTube [2] and the
irrelevant ones are manually removed. All clips have fixed
frame rate and resolution of 25 FPS and 320 × 240 respectively. The videos are saved in .avi files compressed using DivX codec available in k-lite package [1]. The audio
is preserved for the clips of the new 51 actions. Table 1
summarizes the characteristics of the dataset.
Actions
Clips
Groups per Action
Clips per Group
Mean Clip Length
Total Duration
Min Clip Length
Max Clip Length
Frame Rate
Resolution
Audio
101
13320
25
4-7
7.21 sec
1600 mins
1.06 sec
71.04 sec
25 fps
320×240
Yes (51 actions)
Table 1. Summary of Characteristics of UCF101
Naming Convention: The zipped file of the dataset
(available
at
http://crcv.ucf.edu/data/
UCF101.php ) includes 101 folders each containing
the clips of one action class. The name of each clip has the
following form:
v X gY cZ.avi
2500
Time (sec)
2000
16
Total Time
Average Clip Duration
14
12
10
1500
8
1000
6
4
500
2
0
ApplyEyeMakeup
ApplyLipstick
Archery
BabyCrawling
BalanceBeam
BandMarching
BaseballPitch
Basketball
BasketballDunk
BenchPress
Biking
Billiards
BlowDryHair
BlowingCandles
BodyWeightSquats
Bowling
BoxingPunchingBag
BoxingSpeedBag
BreastStroke
BrushingTeeth
CleanAndJerk
CliffDiving
CricketBowling
CricketShot
CuttingInKitchen
Diving
Drumming
Fencing
FieldHockeyPenalty
FloorGymnastics
FrisbeeCatch
FrontCrawl
GolfSwing
Haircut
Hammering
HammerThrow
HandstandPushups
HandstandWalking
HeadMassage
HighJump
HorseRace
HorseRiding
HulaHoop
IceDancing
JavelinThrow
JugglingBalls
JumpingJack
JumpRope
Kayaking
Knitting
0
2500
Time (sec)
2000
16
Total Time
Average Clip Duration
14
12
10
1500
8
1000
6
4
500
2
0
LongJump
Lunges
MilitaryParade
Mixing
MoppingFloor
Nunchucks
ParallelBars
PizzaTossing
PlayingCello
PlayingDaf
PlayingDhol
PlayingFlute
PlayingGuitar
PlayingPiano
PlayingSitar
PlayingTabla
PlayingViolin
PoleVault
PommelHorse
PullUps
Punch
PushUps
Rafting
RockClimbingIndoor
RopeClimbing
Rowing
SalsaSpin
ShavingBeard
Shotput
SkateBoarding
Skiing
Skijet
SkyDiving
SoccerJuggling
SoccerPenalty
StillRings
SumoWrestling
Surfing
Swing
TableTennisShot
TaiChi
TennisSwing
ThrowDiscus
TrampolineJumping
Typing
UnevenBars
VolleyballSpiking
WalkingWithDog
WallPushups
WritingOnBoard
YoYo
0
Figure 4. Total time of videos for each class is illustrated using the blue bars. The average length of the clips for each action is depicted in
green.
where X, Y and Z represent action class label,
group and clip number respectively.
For instance,
v ApplyEyeMakeup g03 c04.avi corresponds to
the clip 4 of group 3 of action class ApplyEyeMakeup.
3. Experimental Results
We performed an experiment using bag of words approach which is widely accepted as a standard action recognition method to provide baseline results on UCF101.
From each clip, we extracted Harris3D corners (using
the implementation by [7]) and computed 162 dimensional
HOG/HOF descriptors for each. We clustered a randomly
selected set of 100,000 space-time interest points (STIP) using k-means to build the codebook. The size of our codebook is k=4000 which is shown to yield good results over
a wide range of datasets. The descriptors were assigned
to their closest video words using nearest neighbor classifier, and each clip was represented by a 4000-dimensional
histogram of its words. Utilizing three train/test splits, a
SVM was trained using the histogram vectors of the train-
ing set. We employed a nonlinear multiclass SVM with histogram intersection kernel and 101 classes each representing one action. For testing, a similar histogram representation for the query video was computed and classified using
the trained SVM. This method yielded an overall accuracy
of 43.9%; The confusion matrix for all 101 actions is shown
in Fig. 5.
The accuracy for the predefined action types are:
Sports (49.40%), Playing Musical Instrument (42.04%),
Human-Object Interaction (36.62%), Body-Motion Only
(37.64%), Human-Human Interaction (42.66%). Sports actions achieve the highest accuracy since performing sports
typically requires distinctive motions which makes the classification easier. Moreover, the background in sports clips
are generally less cluttered compared to other action types.
Unlike Sports Actions, Human-Object Interaction clips typically have a highly cluttered background. Additionally, the
informative motions typically occupy a small portion of the
motions in the clips which explains the low recognition accuracy of this action class.
Dataset
KTH [11]
Weizmann [3]
UCF Sports [10]
IXMAS [12]
UCF11 [6]
HOHA [7]
Olympic [8]
UCF50 [9]
HMDB51 [5]
UCF101
Number of Actions
6
9
9
11
11
12
16
50
51
101
Clips
600
81
182
165
1168
2517
800
6681
6766
13320
Background
Static
Static
Dynamic
Static
Dynamic
Dynamic
Dynamic
Dynamic
Dynamic
Dynamic
Camera Motion
Slight
No
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Release Year
2004
2005
2009
2006
2009
2009
2010
2010
2011
2012
Resource
Actor Staged
Actor Staged
TV, Movies
Actor Staged
YouTube
Movies
YouTube
YouTube
Movies, YouTube, Web
YouTube
Table 2. Summary of Major Action Recognition Datasets
We recommend a three train/test split (available
at:
http://crcv.ucf.edu/data/UCF101/
UCF101TrainTestSplits-RecognitionTask.
zip ) experimental setup to keep consistency of the
reported tests on UCF101; the baseline results provided in
this section were computed using the same scenario. These
train/test splits have been designed in a way to keep the
groups separate, hence not sharing the clips from the same
group in training and testing, as the clips within a group
are obtained from a single long video. Each test split has 7
different groups and their respective remaining 18 groups
are used for training.
The above experiment was also performed using a leaveone-group-out 25-fold cross validation setup, giving an
overall accuracy of 44.5%. By testing on one group and
training on the rest, it was made sure that the clips from a
group are not divided between training and testing set.
4. Related Datasets
UCF Sports, UCF11, UCF50 and UCF101 are the four
action datasets compiled by UCF in chronological order;
each one includes its precursor. We made two minor modifications in the portion of UCF101 which includes UCF50
videos: the number of groups is fixed to 25 for all the actions, and each group includes up to 7 clips. Table 2 shows
a list of existing action recognition datasets with detailed
characteristics of each. Note that UCF101 is remarkably
larger than the rest.
5. Conclusion
We introduced UCF101 which is the most challenging dataset for action recognition compared to the existing ones. It includes 101 action classes and over 13k clips
which makes it outstandingly larger than other datasets.
UCF101 is composed of unconstrained videos downloaded
from YouTube which feature challenges such as poor lighting, cluttered background and severe camera motion. We
provided baseline action recognition results on this new
dataset using standard bag of words method with overall
accuracy of 43.9%.
References
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[2] Youtube. http://www.youtube.com/. 3
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Actions as space-time shapes, 2005. International Conference on Computer Vision (ICCV). 1, 5
[4] G. Johansson, S. Bergstrom, and W. Epstein. Perceiving
events and objects, 1994. Lawrence Erlbaum Associates. 1
[5] H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre.
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(ICCV). 1, 5
[6] J. Liu, J. Luo, and M. Shah. Recognizing realistic actions
from videos in the wild, 2009. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1, 5
[7] M. Marszaek, I. Laptev, and C. Schmid. Actions in context,
2009. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). 1, 4, 5
[8] J. Niebles, C. Chen, and L. Fei-Fei. Modeling temporal
structure of decomposable motion segments for activity classication, 2010. European Conference on Computer Vision
(ECCV). 1, 5
[9] K. Reddy and M. Shah. Recognizing 50 human action categories of web videos, 2012. Machine Vision and Applications Journal (MVAP). 1, 5
[10] M. Rodriguez, J. Ahmed, and M. Shah. Action mach: A
spatiotemporal maximum average correlation height lter for
action recognition, 2008. IEEE Conference on Computer
Vision and Pattern Recognition (CVPR). 1, 5
[11] C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach, 2004. International Conference
on Pattern Recognition (ICPR). 1, 5
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from arbitrary views using 3d exemplars, 2007. International
Conference on Computer Vision (ICCV). 1, 5
Head Massage
Salsa Spins
Band Marching
Walking with a Dog
Tai Chi
Rope Climbing
Push Ups
Lunges
Handstand Walking
Body Weight Squats
Baby Crawling
Writing On Board
Soccer Juggling
Shaving Beard
Nun chucks
Mixing Batter
Jump Rope
Cutting In Kitchen
Hula Hoop
Blow Dry Hair
Apply Eye Makeup
Playing Flute
Playing Daf
Playing Violin
Drumming
Playing Piano
Uneven Bars
Tennis Swing
Surfing
Still Rings
SkyDiving
Skiing
Punch
Rowing
Pole Vault
Long Jump
Horse Riding
Javelin Throw
High Jump
Golf Swing
Frisbee Catch
Field Hockey Penalty
Diving
Cricket Bowling
Clean and Jerk
Bowling
Boxing Speed Bag
Biking
Baseball Pitch
Basketball Dunk
Archery
Archery
Balance Beam
Baseball Pitch
Basketball Shooting
Basketball Dunk
Bench Press
Biking
Billiards Shot
Bowling
Boxing Punching Bag
Boxing Speed Bag
0.9
Breaststroke
Clean and Jerk
Cliff Diving
Cricket Bowling
Cricket Shot
Diving
Fencing
Field Hockey Penalty
Floor Gymnastics
Frisbee Catch
0.8
Front Crawl
Golf Swing
Hammer Throw
High Jump
Horse Race
Javelin Throw
Ice Dancing
Horse Riding
Kayaking
Long Jump
0.7
Parallel Bars
Pole Vault
Pommel Horse
Punch
Rafting
Rowing
Shotput
Skiing
Skijet
Sky Diving
Soccer Penalty
Still Rings
0.6
SumoWrestling
Surfing
Table Tennis Shot
Tennis Swing
Throw Discus
Uneven Bars
Volleyball Spiking
Drumming
Playing Guitar
Playing Piano
0.5
Playing Tabla
Playing Violin
Playing Cello
Playing Daf
Playing Dhol
Playing Flute
Playing Sitar
Apply Eye Makeup
Apply Lipstick
Blow Dry Hair
0.4
Brushing Teeth
Cutting In Kitchen
Hammering
Hula Hoop
Juggling Balls
Jump Rope
Knitting
Mixing Batter
Mopping Floor
Nun chucks
0.3
Pizza Tossing
Shaving Beard
Skate Boarding
Soccer Juggling
Typing
Writing On Board
Yo Yo
Baby Crawling
Blowing Candles
BodyWeight Squats
0.2
Handstand Pushups
Handstand Walking
Jumping Jack
Lunges
Pull Ups
Push Ups
Rock Climbing Indoor
Rope Climbing
Swing
Tai Chi
0.1
Trampoline Jumping
Walking with a Dog
Wall Pushups
Band Marching
Haircut
Head Massage
Military Parade
Salsa Spins
Military Parade
Haircut
Wall Pushups
Trampoline Jumping
Swing
Rock Climbing Indoor
Pull Ups
Jumping Jack
Handstand Pushups
Blowing Candles
Yo Yo
Typing
Skate Boarding
Pizza Tossing
Knitting
Mopping Floor
Juggling Balls
'Hammering
Brushing Teeth
Apply Lipstick
Playing Sitar
Playing Dhol
Playing Cello
Playing Tabla
Playing Guitar
Volleyball Spiking
Throw Discus
Table Tennis Shot
Sumo Wrestling
Skijet
Soccer Penalty
Shotput
Rafting
Pommel Horse
Parallel Bars
Kayaking
Ice Dancing
Horse Race
Hammer Throw
Front Crawl
Floor Gymnastics
Fencing
Cliff Diving
Cricket Shot
Breaststroke
Billiards Shot
Boxing Punching Bag
Bench Press
Basketball Shooting
Balance Beam
0
Figure 5. Confusion table of baseline action recognition results using bag of words approach on UCF101. The drawn lines separate different
types of actions; 1-50: Sports, 51-60: Playing Musical Instrument, 61-80: Human-Object Interaction, 81-96: Body-Motion Only, 97-101:
Human-Human Interaction.