  Person Re-Identification (Re-ID) aims to search for a person of interest
(query) in a network of cameras. In the classic Re-ID setting the query is
sought in a gallery containing properly cropped images of entire bodies.
Recently, the live Re-ID setting was introduced to represent the practical
application context of Re-ID better. It consists in searching for the query in
short videos, containing whole scene frames. The initial live Re-ID baseline
used a pedestrian detector to build a large search gallery and a classic Re-ID
model to find the query in the gallery. However, the galleries generated were
too large and contained low-quality images, which decreased the live Re-ID
performance. Here, we present a new live Re-ID approach called TrADe, to
generate lower high-quality galleries. TrADe first uses a Tracking algorithm to
identify sequences of images of the same individual in the gallery. Following,
an Anomaly Detection model is used to select a single good representative of
each tracklet. TrADe is validated on the live Re-ID version of the PRID-2011
dataset and shows significant improvements over the baseline.
