  Existing person re-identification methods rely on the visual sensor to
capture the pedestrians. The image or video data from visual sensor inevitably
suffers the occlusion and dramatic variations of pedestrian postures, which
degrades the re-identification performance and further limits its application
to the open environment. On the other hand, for most people, one of the most
important carry-on items is the mobile phone, which can be sensed by WiFi and
cellular networks in the form of a wireless positioning signal. Such signal is
robust to the pedestrian occlusion and visual appearance change, but suffers
some positioning error. In this work, we approach person re-identification with
the sensing data from both vision and wireless positioning. To take advantage
of such cross-modality cues, we propose a novel recurrent context propagation
module that enables information to propagate between visual data and wireless
positioning data and finally improves the matching accuracy. To evaluate our
approach, we contribute a new Wireless Positioning Person Re-identification
(WP-ReID) dataset. Extensive experiments are conducted and demonstrate the
effectiveness of the proposed algorithm. Code will be released at
https://github.com/yolomax/WP-ReID.
