Person re-identification (re-id), the process of matching pedestrian images
across different camera views, is an important task in visual surveillance.
Substantial development of re-id has recently been observed, and the majority
of existing models are largely dependent on color appearance and assume that
pedestrians do not change their clothes across camera views. This limitation,
however, can be an issue for re-id when tracking a person at different places
and at different time if that person (e.g., a criminal suspect) changes his/her
clothes, causing most existing methods to fail, since they are heavily relying
on color appearance and thus they are inclined to match a person to another
person wearing similar clothes. In this work, we call the person re-id under
clothing change the "cross-clothes person re-id". In particular, we consider
the case when a person only changes his clothes moderately as a first attempt
at solving this problem based on visible light images; that is we assume that a
person wears clothes of a similar thickness, and thus the shape of a person
would not change significantly when the weather does not change substantially
within a short period of time. We perform cross-clothes person re-id based on a
contour sketch of person image to take advantage of the shape of the human body
instead of color information for extracting features that are robust to
moderate clothing change. Due to the lack of a large-scale dataset for
cross-clothes person re-id, we contribute a new dataset that consists of 33698
images from 221 identities. Our experiments illustrate the challenges of
cross-clothes person re-id and demonstrate the effectiveness of our proposed
method.

通过轮廓素描提取人体形状特征进行跨服装人物再识别，训练新的数据集，并在实验中展示了我们的方法的有效性。