  Many unsupervised approaches have been proposed recently for the video-based
re-identification problem since annotations of samples across cameras are
time-consuming. However, higher-order relationships across the entire camera
network are ignored by these methods, leading to contradictory outputs when
matching results from different camera pairs are combined. In this paper, we
address the problem of unsupervised video-based re-identification by proposing
a consistent cross-view matching (CCM) framework, in which global camera
network constraints are exploited to guarantee the matched pairs are with
consistency. Specifically, we first propose to utilize the first neighbor of
each sample to discover relations among samples and find the groups in each
camera. Additionally, a cross-view matching strategy followed by global camera
network constraints is proposed to explore the matching relationships across
the entire camera network. Finally, we learn metric models for camera pairs
progressively by alternatively mining consistent cross-view matching pairs and
updating metric models using these obtained matches. Rigorous experiments on
two widely-used benchmarks for video re-identification demonstrate the
superiority of the proposed method over current state-of-the-art unsupervised
methods; for example, on the MARS dataset, our method achieves an improvement
of 4.2\% over unsupervised methods, and even 2.5\% over one-shot
supervision-based methods for rank-1 accuracy.
