Time Series Datasets

UCF50 & UCF101

UCF50 & UCF101:

These two datasets contain realistic action recognition videos collected from Youtube with large variations in motion, pose, scales and conditions. The video files are categorized in groups with similar features, for example same person in the videos, similar viewpoints, background, etc.

UCF50

Here is some information regarding this dataset:

  • Number of Categories: 50 categories provided in bellow (copied from the original page)

  • Number of Groups: 25 (more than 4 clips for every action in each group)

Categories: “Baseball Pitch, Basketball Shooting, Bench Press, Biking, Biking, Billiards Shot,Breaststroke, Clean and Jerk, Diving, Drumming, Fencing, Golf Swing, Playing Guitar, High Jump, Horse Race, Horse Riding, Hula Hoop, Javelin Throw, Juggling Balls, Jump Rope, Jumping Jack, Kayaking, Lunges, Military Parade, Mixing Batter, Nun chucks, Playing Piano, Pizza Tossing, Pole Vault, Pommel Horse, Pull Ups, Punch, Push Ups, Rock Climbing Indoor, Rope Climbing, Rowing, Salsa Spins, Skate Boarding, Skiing, Skijet, Soccer Juggling, Swing, Playing Tabla, TaiChi, Tennis Swing, Trampoline Jumping, Playing Violin, Volleyball Spiking, Walking with a dog, and Yo Yo”.

More details about this dataset and links of download can be found on http://crcv.ucf.edu/data/UCF50.php.

If you use this dataset, make sure to cite the paper:

K. K. Reddy, M. Shah, Recognizing 50 Human Action Categories of Web Videos, Machine Vision and Applications Journal (MVAP), 2012.

UCF101

Here is some information regarding this dataset:

  • Number of Video Clips: 13320

  • Number of Categories: 101 categories provided in bellow (copied from the original page)

  • Number of Groups: 25 (4-7 clips for every action in each group)

Categories: “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”.

More details about this dataset and links of download can be found on http://crcv.ucf.edu/data/UCF101.php.

If you use this dataset, please refer to the technical report:

K. Soomro, A. Roshan Zamir, M. Shah, UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild, CRCV-TR-12-01, 2012.

Keywords: Vision, Action Recognition, Time Series, Video, Youtube, Sports, Human Interaction, Body Motion

WESAD: Wearable Stress and Affect Detection

WESAD: Wearable Stress and Affect Detection:

This dataset contains physiological and motion data of 15 subjects collected by using a wrist and a chest device worn. The chest-worn device records ECG, Electrodermal Activity, Electromyogram, Respiration, Body Temperature and Three-access Acceleration and the wrist-worn device records Blood Volume Pulse, Electrodermal activity, Body Temperature and Three-axis Acceleration. More details about the dataset and the links of download can be found on https://archive.ics.uci.edu/ml/datasets/WESAD+%28Wearable+Stress+and+Affect+Detection%29.

Here is some information regarding the dataset:

  • Number of Instances: 63,000,000

  • Number of Attributes: 12

  • Number of Subjects: 15

If you use this dataset:

Make sure to use the data for academic research and non-commercial purposes only.

Make sure to cite the paper:

P. Schmidt, A. Reiss, R. Duerchen, C. Marberger, K. V. Laerhoven, Intorducing WESAD: a Multimodal Dataset for Wearable Stress and Affect Detection, International Conference on Multimodal Interaction (ICMI), 2018.

Keywords: Biology and Health, Classification, Regression, Stress Detection, Motion, Time Series

Infectious Disease Spread: Flu

Infectious Disease Spread: Flu

This dataset contains information about the Flu virus spreading between healthy and infected students by having close interactions. The nodes refer to almost the entire school population and the edges refer to the interactions with different durations. Most of the contacts are short time. More information about the dataset and links of download can be found on http://sing.stanford.edu/flu/ and the two publications on the dataset.

Here is some information regarding the dataset:

  • Number of Nodes: 788 individuals (655 students and 73 teachers, 55 staff, 5 other)

  • Number of Edges: 2,148,199 Close Proximity Records (762,868 interactions with a mean duration of 2.8 CPRs (~1min) or 118,291 interactions with mean duration of 18.7 CPRs (~6min)

Detailed information about the dataset can be found on the papers:

M. Salathe, M. Kazandjieva, J. W. Lee, P. Levis, M. W. Feldman, J. H. jones, A High-Resolution Human Contact Network for Infectious Disease Transmission, In Proceedings of National Academy of Science (PNAS), 2010.

m. Kazandjieva, J. W. Lee, M. Salathe, M. W. Feldman, J. H. Jones, P. Levis, Experiences in Measuring a Human Contact Network for Epidemiology Research, Proceedings of the ACM Workshop on Hot Topics in Embedded Networked Sensors (HotEmNets), 2010.

Keywords: Network, Biology and Health, Spreading Phenomena, Epidemic Process, Disease, Flu, Time Series