  In this paper, we make the very first attempt to investigate the integration
of deep hash learning with vehicle re-identification. We propose a deep
hash-based vehicle re-identification framework, dubbed DVHN, which
substantially reduces memory usage and promotes retrieval efficiency while
reserving nearest neighbor search accuracy. Concretely,~DVHN directly learns
discrete compact binary hash codes for each image by jointly optimizing the
feature learning network and the hash code generating module. Specifically, we
directly constrain the output from the convolutional neural network to be
discrete binary codes and ensure the learned binary codes are optimal for
classification. To optimize the deep discrete hashing framework, we further
propose an alternating minimization method for learning binary
similarity-preserved hashing codes. Extensive experiments on two widely-studied
vehicle re-identification datasets- \textbf{VehicleID} and \textbf{VeRi}-~have
demonstrated the superiority of our method against the state-of-the-art deep
hash methods. \textbf{DVHN} of $2048$ bits can achieve 13.94\% and 10.21\%
accuracy improvement in terms of \textbf{mAP} and \textbf{Rank@1} for
\textbf{VehicleID (800)} dataset. For \textbf{VeRi}, we achieve 35.45\% and
32.72\% performance gains for \textbf{Rank@1} and \textbf{mAP}, respectively.
