The vehicle re-identification (ReID) plays a critical role in the perception
system of autonomous driving, which attracts more and more attention in recent
years. However, to our best knowledge, there is no existing complete solution
for the surround-view system mounted on the vehicle. In this paper, we argue
two main challenges in above scenario: i) In single camera view, it is
difficult to recognize the same vehicle from the past image frames due to the
fisheye distortion, occlusion, truncation, etc. ii) In multi-camera view, the
appearance of the same vehicle varies greatly from different camera's
viewpoints. Thus, we present an integral vehicle Re-ID solution to address
these problems. Specifically, we propose a novel quality evaluation mechanism
to balance the effect of tracking box's drift and target's consistency.
Besides, we take advantage of the Re-ID network based on attention mechanism,
then combined with a spatial constraint strategy to further boost the
performance between different cameras. The experiments demonstrate that our
solution achieves state-of-the-art accuracy while being real-time in practice.
Besides, we will release the code and annotated fisheye dataset for the benefit
of community.

本文提出了一个完整的车辆重识别解决方案，旨在解决单目相机视角下的车辆识别困难以及多相机视角下同一车辆外观变化大的问题，并通过实验表明我们的解决方案在实践中实现了最先进的准确性。