  In this paper, we propose a novel feature learning framework for video person
re-identification (re-ID). The proposed framework largely aims to exploit the
adequate temporal information of video sequences and tackle the poor spatial
alignment of moving pedestrians. More specifically, for exploiting the temporal
information, we design a temporal residual learning (TRL) module to
simultaneously extract the generic and specific features of consecutive frames.
The TRL module is equipped with two bi-directional LSTM (BiLSTM), which are
respectively responsible to describe a moving person in different aspects,
providing complementary information for better feature representations. To deal
with the poor spatial alignment in video re-ID datasets, we propose a
spatial-temporal transformer network (ST^2N) module. Transformation parameters
in the ST^2N module are learned by leveraging the high-level semantic
information of the current frame as well as the temporal context knowledge from
other frames. The proposed ST^2N module with less learnable parameters allows
effective person alignments under significant appearance changes. Extensive
experimental results on the large-scale MARS, PRID2011, ILIDS-VID and SDU-VID
datasets demonstrate that the proposed method achieves consistently superior
performance and outperforms most of the very recent state-of-the-art methods.
