Research Data Leeds Repository

Citation

Tayyub, Jawad and Hawasly, Majd and Cohn, Anthony G. (2017) Complex and Long Activities Dataset. University of Leeds. [Dataset] https://doi.org/10.5518/249

Dataset description

An activity dataset is presented here which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions. The dataset consists of a set of videos of actors performing everyday activities in a natural and unscripted manner. The dataset was recorded using a static Kinect 2 sensor which is commonly used on many robotic platforms. The dataset comprises of RGB-D images, point cloud data, automatically generated skeleton tracks in addition to crowdsourced annotations. We believe that this dataset is particularly suitable as a testbed for activity recognition research but it can also be applicable for other common tasks in robotics/computer vision research such as object detection and human skeleton tracking.

Keywords:	activity recognition, daily living activities, video
Divisions:	Faculty of Engineering and Physical Sciences > School of Computing
Related resources:	Location Type https://arxiv.org/abs/1709.03456 Publication https://doi.org/10.1109/WACV.2018.00182 Publication https://eprints.whiterose.ac.uk/128307/ Publication
Citation:	J. Tayyub, M. Hawasly, D. C. Hogg and A. G. Cohn, "Learning Hierarchical Models of Complex Daily Activities from Annotated Videos," 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 2018, pp. 1633-1641
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	24 Sep 2017 20:05
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/254
Additional details Additional details Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Tayyub, Jawad University of Leeds https://ror.org/024mrxd33 ror Hawasly, Majd https://orcid.org/0000-0003-1823-5580 Five AI Cohn, Anthony G. https://orcid.org/0000-0002-7652-8907 University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Publication date: 2017 Resource language: English Metadata language: English Publisher: University of Leeds Contact email: sc12jbmt@leeds.ac.uk Last Modified: 12 Aug 2024 12:51

Files

Documentation

Description of the file structure [100B] Description of the file structure [100B] Description of the file structure [100B] Security: Anyone File name: readme.txt Content type: Documentation Mime-Type: text/plain License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 100B

Data

Crowdsourced annotations [219kB] Crowdsourced annotations [219kB] Crowdsourced annotations [219kB] Security: Anyone File name: annotations.zip Content type: Data Description: These files contain crowdsourced annotations of the videos with start, end frames and activity labels of identifies videos. Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 219kB

Compressed videos [83MB] Compressed videos [83MB] Compressed videos [83MB] Security: Anyone File name: videos.zip Content type: Data Description: These are .mp4 compressed files of all the videos. Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 83MB

Skeleton Tracks [166MB] Skeleton Tracks [166MB] Skeleton Tracks [166MB] Security: Anyone File name: skeletons.zip Content type: Data Description: These are skeleton tracks of the people in each videos in the resolution of raw images Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 166MB

Sample [10GB] Sample [10GB] Sample [10GB] Security: Anyone File name: sample.zip Content type: Data Description: A small sample of the dataset Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 10GB