Most existing person re-identification (ReID) methods rely only on the
spatial appearance information from either one or multiple person images,
whilst ignore the space-time cues readily available in video or image-sequence
data. Moreover, they often assume the availability of exhaustively labelled
cross-view pairwise data for every camera pair, making them non-scalable to
ReID applications in real-world large scale camera networks. In this work, we
introduce a novel video based person ReID method capable of accurately matching
people across views from arbitrary unaligned image-sequences without any
labelled pairwise data. Specifically, we introduce a new space-time person
representation by encoding multiple granularities of spatio-temporal dynamics
in form of time series. Moreover, a Time Shift Dynamic Time Warping (TS-DTW)
model is derived for performing automatically alignment whilst achieving data
selection and matching between inherently inaccurate and incomplete sequences
in a unified way. We further extend the TS-DTW model for accommodating multiple
feature-sequences of an image-sequence in order to fuse information from
different descriptions. Crucially, this model does not require pairwise
labelled training data (i.e. unsupervised) therefore readily scalable to large
scale camera networks of arbitrary camera pairs without the need for exhaustive
data annotation for every camera pair. We show the effectiveness and advantages
of the proposed method by extensive comparisons with related state-of-the-art
approaches using two benchmarking ReID datasets, PRID2011 and iLIDS-VID.

本文提出了一种基于时间序列的视频人员重识别方法，可以准确地匹配任意不对齐的图像序列中的人，无需标记的成对数据，并通过引入时间移动动态时间扭曲 (TS-DTW) 模型实现自动对齐、数据选择和匹配。