  We present the Multiview Extended Video with Activities (MEVA) dataset, a new
and very-large-scale dataset for human activity recognition. Existing security
datasets either focus on activity counts by aggregating public video
disseminated due to its content, which typically excludes same-scene background
video, or they achieve persistence by observing public areas and thus cannot
control for activity content. Our dataset is over 9300 hours of untrimmed,
continuous video, scripted to include diverse, simultaneous activities, along
with spontaneous background activity. We have annotated 144 hours for 37
activity types, marking bounding boxes of actors and props. Our collection
observed approximately 100 actors performing scripted scenarios and spontaneous
background activity over a three-week period at an access-controlled venue,
collecting in multiple modalities with overlapping and non-overlapping indoor
and outdoor viewpoints. The resulting data includes video from 38 RGB and
thermal IR cameras, 42 hours of UAV footage, as well as GPS locations for the
actors. 122 hours of annotation are sequestered in support of the NIST Activity
in Extended Video (ActEV) challenge; the other 22 hours of annotation and the
corresponding video are available on our website, along with an additional 306
hours of ground camera data, 4.6 hours of UAV data, and 9.6 hours of GPS logs.
Additional derived data includes camera models geo-registering the outdoor
cameras and a dense 3D point cloud model of the outdoor scene. The data was
collected with IRB oversight and approval and released under a CC-BY-4.0
license.
