网址：https://www.crcv.ucf.edu/data/UCF101.php
There will be a workshop in ICCV'13 with UCF101 as its main competition benchmark: The First International Workshop on Action Recognition with Large Number of Classes.
Click here to check the published results on UCF101 (updated October 17, 2013)
UCF101是从油管上收集的包含101种动作分类的视频数据集，用于action recognition。包括了13320个视频。
这些视频被分成了25组，每组包括4-7个视频。同组的视频有一些共同的特征，比如相似的背景，相似的视角，等等。
动作种类分为5种：1)人与物互动；2)只有肢体动作；3)人与人互动；4)演奏乐器；5)运动。
包括动作包括：眼部化妆，涂口红，射箭、婴儿爬行、平衡木、乐队、棒球场、篮球投篮、篮球扣篮、卧推、自行车、台球球，吹干头发，吹蜡烛，体重深蹲、保龄球、拳击沙袋、拳击袋、蛙泳、刷牙、挺举、悬崖跳水，板球保龄球、板球拍，厨房、潜水、击鼓、击剑切割，曲棍球点球，体操，飞盘接住，爬泳，高尔夫挥杆、发型、链球、锤击、倒立俯卧撑，散步，头部按摩，跳高，马种族、骑马、Hula Hoop、冰上舞蹈、标枪、杂耍球、跳绳、跳杰克、凯国王、针织、跳远、弓步、阅兵、Mixing Batter、拖地、尼姑夹头，双杠，比萨折腾、弹吉他、弹钢琴、打手鼓，小提琴，大提琴，演奏Daf，都玩，吹笛，打Sitar，撑竿跳高，Pommel Horse，拉，冲，Push Ups、漂流、攀岩、室内攀绳，划船，莎莎旋转，刮胡子，铅球，滑板，滑雪，skijet、跳伞、足球杂耍、足球点球、吊环、相扑、冲浪、秋千、乒乓球、太极拳、网球摆、掷铁饼、跳蹦蹦床、打字、不均匀酒吧，排球扣球，一只狗，走墙俯卧撑，板书，Yo Yo。
UCF101 is an action recognition data set of realistic action videos, collected from YouTube, having 101 action categories. This data set is an extension of UCF50 data set which has 50 action categories.
With 13320 videos from 101 action categories, UCF101 gives the largest diversity in terms of actions and with the presence of large variations in camera motion, object appearance and pose, object scale, viewpoint, cluttered background, illumination conditions, etc, it is the most challenging data set to date. As most of the available action recognition data sets are not realistic and are staged by actors, UCF101 aims to encourage further research into action recognition by learning and exploring new realistic action categories.
The videos in 101 action categories are grouped into 25 groups, where each group can consist of 4-7 videos of an action. The videos from the same group may share some common features, such as similar background, similar viewpoint, etc.
The action categories can be divided into five types: 1)Human-Object Interaction 2) Body-Motion Only 3) Human-Human Interaction 4) Playing Musical Instruments 5) Sports.
The action categories for UCF101 data set are: Apply Eye Makeup, Apply Lipstick, Archery, Baby Crawling, Balance Beam, Band Marching, Baseball Pitch, Basketball Shooting, Basketball Dunk, Bench Press, Biking, Billiards Shot, Blow Dry Hair, Blowing Candles, Body Weight Squats, Bowling, Boxing Punching Bag, Boxing Speed Bag, Breaststroke, Brushing Teeth, Clean and Jerk, Cliff Diving, Cricket Bowling, Cricket Shot, Cutting In Kitchen, Diving, Drumming, Fencing, Field Hockey Penalty, Floor Gymnastics, Frisbee Catch, Front Crawl, Golf Swing, Haircut, Hammer Throw, Hammering, Handstand Pushups, Handstand Walking, Head Massage, High Jump, Horse Race, Horse Riding, Hula Hoop, Ice Dancing, Javelin Throw, Juggling Balls, Jump Rope, Jumping Jack, Kayaking, Knitting, Long Jump, Lunges, Military Parade, Mixing Batter, Mopping Floor, Nun chucks, Parallel Bars, Pizza Tossing, Playing Guitar, Playing Piano, Playing Tabla, Playing Violin, Playing Cello, Playing Daf, Playing Dhol, Playing Flute, Playing Sitar, Pole Vault, Pommel Horse, Pull Ups, Punch, Push Ups, Rafting, Rock Climbing Indoor, Rope Climbing, Rowing, Salsa Spins, Shaving Beard, Shotput, Skate Boarding, Skiing, Skijet, Sky Diving, Soccer Juggling, Soccer Penalty, Still Rings, Sumo Wrestling, Surfing, Swing, Table Tennis Shot, Tai Chi, Tennis Swing, Throw Discus, Trampoline Jumping, Typing, Uneven Bars, Volleyball Spiking, Walking with a dog, Wall Pushups, Writing On Board, Yo Yo.
The UCF101 data set can be downloaded by clicking here.
Revised annotations have been made available at http://www.thumos.info/download.html
The Train/Test Splits for Action Recognition on UCF101 data set can be downloaded by clicking here.
The Train/Test Splits for Action Detection on UCF101 data set can be downloaded by clicking here.
The STIP Features for UCF101 data set can be downloaded here: Part1 Part2.
If you use this data set, please refer to the following technical report:
Khurram Soomro, Amir Roshan Zamir and Mubarak Shah, UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild., CRCV-TR-12-01, November, 2012.
For questions regarding this data set, please contact Khurram Soomro (khurram [at] knights.ucf.edu).
Statistics
Results on UCF101
If you happen to use UCF101, send us an email with the following details and we will update our webpage with your results.
- Performance (%)
- Experimental Setup (In order to keep the reported results consistent, please follow "Three Train/Test Splits". This would eliminate randomness in the experimental setup.)
- Paper details
| Performance | Experimental Setup | Paper |
|---|---|---|
(CRCV-TR-12-01),2012 |
||
Note: It is very important to keep the videos belonging to the same group seperate in training and testing. Since the videos in a group are obtained from single long video, sharing videos from same group in training and testing sets would give high performance.