  Vehicle re-identification is an important problem and has many applications
in video surveillance and intelligent transportation. It gains increasing
attention because of the recent advances of person re-identification
techniques. However, unlike person re-identification, the visual differences
between pairs of vehicle images are usually subtle and even challenging for
humans to distinguish. Incorporating additional spatio-temporal information is
vital for solving the challenging re-identification task. Existing vehicle
re-identification methods ignored or used over-simplified models for the
spatio-temporal relations between vehicle images. In this paper, we propose a
two-stage framework that incorporates complex spatio-temporal information for
effectively regularizing the re-identification results. Given a pair of vehicle
images with their spatio-temporal information, a candidate
visual-spatio-temporal path is first generated by a chain MRF model with a
deeply learned potential function, where each visual-spatio-temporal state
corresponds to an actual vehicle image with its spatio-temporal information. A
Siamese-CNN+Path-LSTM model takes the candidate path as well as the pairwise
queries to generate their similarity score. Extensive experiments and analysis
show the effectiveness of our proposed method and individual components.
