The large-scale MEVA dataset is designed for activity detection in multi-camera environments. It was created on the Intelligence Advanced Research Projects Activity (IARPA) Deep Intermodal Video Analytics (DIVA) program to support DIVA performers and the broader research community.
MEVA aims to build a corpus of activity video collected from multiple viewpoints in realistic settings.
There is a MEVA data users Google group to facilitate communication and collaboration for those interested in working with the data. Join the conversation through the meva-data-users group.
Known Facility Release #1 ("KF1"):
The KF1 data release contains approximately 185 hours of video collected at the Muscatatuck Urban Training Center with a team of over 100 actors performing in various scenarios. The fields of view, both overlapping and non-overlapping, capture person and vehicle activities in indoor and outdoor environments. There were multiple realistic scenarios with a variety of scripted and non-scripted activities.
The camera infrastructure included commercial-off-the-shelf EO cameras; thermal infrared cameras as part of several IR-EO pairs; and a range of still images from handheld cameras.
Additional resources such as a facility site map, camera model information, and camera calibration models will be also be released.
The video to the right is a montage of randomly selected KF1 clips, re-encoded for faster playback.
The main corpus of 295GB of video files, available for
download via AWS S3-requester-pays for approximately
Download Access Instructions
A selection of sample videos is available, including
one-minute raw samples and scaled versions of complete
Download Example Videos
Various collection metadata, including: a PDF of the site
map with approximate camera locations and sample FOVs, a list
of all files in the KF1 corpus, and camera datasheets.
The MEVA data can be annotated using your preferred annotation toolchain. For annotating and using the MEVA data, the following steps are recommended:
Download and review short clips of visualized annotations for each activity type.Download exemplars
Download the current activity definitions. These should guide which activities and objects are annotated.Download guidelines
Contributing your annotations will increase the utility of the MEVA KF1 dataset for everyone. Please clone our annotation git repository and file a merge request to have your annotations pooled back into the master branch.