  Current person re-identification (ReID) methods typically rely on
single-frame imagery features, whilst ignoring space-time information from
image sequences often available in the practical surveillance scenarios.
Single-frame (single-shot) based visual appearance matching is inherently
limited for person ReID in public spaces due to the challenging visual
ambiguity and uncertainty arising from non-overlapping camera views where
viewing condition changes can cause significant people appearance variations.
In this work, we present a novel model to automatically select the most
discriminative video fragments from noisy/incomplete image sequences of people
from which reliable space-time and appearance features can be computed, whilst
simultaneously learning a video ranking function for person ReID. Using the
PRID$2011$, iLIDS-VID, and HDA+ image sequence datasets, we extensively
conducted comparative evaluations to demonstrate the advantages of the proposed
model over contemporary gait recognition, holistic image sequence matching and
state-of-the-art single-/multi-shot ReID methods.
