  Person re-identification (re-id) is a critical problem in video analytics
applications such as security and surveillance. The public release of several
datasets and code for vision algorithms has facilitated rapid progress in this
area over the last few years. However, directly comparing re-id algorithms
reported in the literature has become difficult since a wide variety of
features, experimental protocols, and evaluation metrics are employed. In order
to address this need, we present an extensive review and performance evaluation
of single- and multi-shot re-id algorithms. The experimental protocol
incorporates the most recent advances in both feature extraction and metric
learning. To ensure a fair comparison, all of the approaches were implemented
using a unified code library that includes 11 feature extraction algorithms and
22 metric learning and ranking techniques. All approaches were evaluated using
a new large-scale dataset that closely mimics a real-world problem setting, in
addition to 16 other publicly available datasets: VIPeR, GRID, CAVIAR,
DukeMTMC4ReID, 3DPeS, PRID, V47, WARD, SAIVT-SoftBio, CUHK01, CHUK02, CUHK03,
RAiD, iLIDSVID, HDA+ and Market1501. The evaluation codebase and results will
be made publicly available for community use.
