  Person re-identification is critical in surveillance applications. Current
approaches rely on appearance based features extracted from a single or
multiple shots of the target and candidate matches. These approaches are at a
disadvantage when trying to distinguish between candidates dressed in similar
colors or when targets change their clothing. In this paper we propose a
dynamics-based feature to overcome this limitation. The main idea is to capture
soft biometrics from gait and motion patterns by gathering dense short
trajectories (tracklets) which are Fisher vector encoded. To illustrate the
merits of the proposed features we introduce three new "appearance-impaired"
datasets. Our experiments on the original and the appearance impaired datasets
demonstrate the benefits of incorporating dynamics-based information with
appearance-based information to re-identification algorithms.
