  Video-based person re-identification (Re-ID) aims to retrieve video sequences
of the same person under non-overlapping cameras. Previous methods usually
focus on limited views, such as spatial, temporal or spatial-temporal view,
which lack of the observations in different feature domains. To capture richer
perceptions and extract more comprehensive video representations, in this paper
we propose a novel framework named Trigeminal Transformers (TMT) for
video-based person Re-ID. More specifically, we design a trigeminal feature
extractor to jointly transform raw video data into spatial, temporal and
spatial-temporal domain. Besides, inspired by the great success of vision
transformer, we introduce the transformer structure for video-based person
Re-ID. In our work, three self-view transformers are proposed to exploit the
relationships between local features for information enhancement in spatial,
temporal and spatial-temporal domains. Moreover, a cross-view transformer is
proposed to aggregate the multi-view features for comprehensive video
representations. The experimental results indicate that our approach can
achieve better performance than other state-of-the-art approaches on public
Re-ID benchmarks. We will release the code for model reproduction.
