  In this paper we evaluate running gait as an attribute for video person
re-identification in a long-distance running event. We show that running gait
recognition achieves competitive performance compared to appearance-based
approaches in the cross-camera retrieval task and that gait and appearance
features are complementary to each other. For gait, the arm swing during
running is less distinguishable when using binary gait silhouettes, due to
ambiguity in the torso region. We propose to use human semantic parsing to
create partial gait silhouettes where the torso is left out. Leaving out the
torso improves recognition results by allowing the arm swing to be more visible
in the frontal and oblique viewing angles, which offers hints that arm swings
are somewhat personal. Experiments show an increase of 3.2% mAP on the
CampusRun and increased accuracy with 4.8% in the frontal and rear view on
CASIA-B, compared to using the full body silhouettes.
