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.
19 March 2020: We've released GPS data associated with the actors in the released video. See here for details.
13 December 2019: We're pleased to announce annotations for an addition 6 hours of MEVA data, resulting in 22 hours of annotated data. Annotations are available via the git repository.
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.
The KF1 data was collected over a total of three weeks at the Muscatatuck Urban Training Center (MUTC) 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; two DJI Inspire 1 v2 drones, and a range of still images from handheld cameras.
The actors were also carrying GPS loggers; see here for more details.
Visualization of the fine-grained MUTC 3D model.
Annotation sample (accelerated for display.)
MEVA video data is hosted on an Amazon Web Services (AWS) S3 bucket; download is provided at no cost via sponsorship through Amazon's AWS Public Dataset Program.
The MEVA data git repository is an evolving collection of metadata and annotations released for the MEVA KF1 data. Highlights include:
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.