  In this paper, we present the Multi-view Extended Videos with Identities
(MEVID) dataset for large-scale, video person re-identification (ReID) in the
wild. To our knowledge, MEVID represents the most-varied video person ReID
dataset, spanning an extensive indoor and outdoor environment across nine
unique dates in a 73-day window, various camera viewpoints, and entity clothing
changes. Specifically, we label the identities of 158 unique people wearing 598
outfits taken from 8, 092 tracklets, average length of about 590 frames, seen
in 33 camera views from the very large-scale MEVA person activities dataset.
While other datasets have more unique identities, MEVID emphasizes a richer set
of information about each individual, such as: 4 outfits/identity vs. 2
outfits/identity in CCVID, 33 viewpoints across 17 locations vs. 6 in 5
simulated locations for MTA, and 10 million frames vs. 3 million for LS-VID.
Being based on the MEVA video dataset, we also inherit data that is
intentionally demographically balanced to the continental United States. To
accelerate the annotation process, we developed a semi-automatic annotation
framework and GUI that combines state-of-the-art real-time models for object
detection, pose estimation, person ReID, and multi-object tracking. We evaluate
several state-of-the-art methods on MEVID challenge problems and
comprehensively quantify their robustness in terms of changes of outfit, scale,
and background location. Our quantitative analysis on the realistic, unique
aspects of MEVID shows that there are significant remaining challenges in video
person ReID and indicates important directions for future research.
