  Most existing person re-identification (re-id) models focus on matching still
person images across disjoint camera views. Since only limited information can
be exploited from still images, it is hard (if not impossible) to overcome the
occlusion, pose and camera-view change, and lighting variation problems. In
comparison, video-based re-id methods can utilize extra space-time information,
which contains much more rich cues for matching to overcome the mentioned
problems. However, we find that when using video-based representation, some
inter-class difference can be much more obscure than the one when using
still-image based representation, because different people could not only have
similar appearance but also have similar motions and actions which are hard to
align. To solve this problem, we propose a top-push distance learning model
(TDL), in which we integrate a top-push constrain for matching video features
of persons. The top-push constraint enforces the optimization on top-rank
matching in re-id, so as to make the matching model more effective towards
selecting more discriminative features to distinguish different persons. Our
experiments show that the proposed video-based re-id framework outperforms the
state-of-the-art video-based re-id methods.
