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.
NEWS:
16 December 2022: MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification is released! We've developed an additional layer of annotations for person re-identification on MEVA video. Additional information and a link to our WACV23 paper may be found below.
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 dataset is described in our WACV 2021 paper. The bibtex citation is:
@InProceedings{Corona_2021_WACV, author = {Corona, Kellie and Osterdahl, Katie and Collins, Roderic and Hoogs, Anthony}, title = {MEVA: A Large-Scale Multiview, Multimodal Video Dataset for Activity Detection}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2021}, pages = {1060-1068} }
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.
Visualizations of MEVA ground truth are available via our DIVE analytics toolchain. Create an account and click here to view MEVA video and its associated ground truth.
Instructions for downloading MEVID annotations and supporting video may be found on https://github.com/Kitware/mevid.
The dataset is described in our paper, MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification, due to appear in WACV 2023. The bibtex citation is:
@InProceedings{Davila_MEVID_2023, author = {Davila, Daniel and Du, Dawei and Lewis, Bryon and Funk, Christopher and Van Pelt, Joseph and Collins, Roderic and Corona, Kellie and Brown, Matt and McCloskey, Scott and Hoogs, Anthony and Clipp, Brian}, title = {MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023} }
Actor checkin photos (top row) are associated with tracklets from MEVA videos (middle and bottom rows) to create global IDs.
Visualization of the fine-grained MUTC 3D model.
Annotation sample (accelerated for display.)
All MEVA data is available for use under a CC BY-4.0 license; the general MEVA data license is available here.
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:
There are several annotation efforts:
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 exemplarsDownload the current activity definitions. These should guide which activities and objects are annotated.
Download guidelinesGenerate schema like these based on the format described here as part of our annotation git repository.
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.