  Vehicle re-identification (Re-ID) is a critical component of the autonomous
driving perception system, and research in this area has accelerated in recent
years. However, there is yet no perfect solution to the vehicle
re-identification issue associated with the car's surround-view camera system.
Our analysis identifies two significant issues in the aforementioned scenario:
i) It is difficult to identify the same vehicle in many picture frames due to
the unique construction of the fisheye camera. ii) The appearance of the same
vehicle when seen via the surround vision system's several cameras is rather
different. To overcome these issues, we suggest an integrative vehicle Re-ID
solution method. On the one hand, we provide a technique for determining the
consistency of the tracking box drift with respect to the target. On the other
hand, we combine a Re-ID network based on the attention mechanism with spatial
limitations to increase performance in situations involving multiple cameras.
Finally, our approach combines state-of-the-art accuracy with real-time
performance. We will soon make the source code and annotated fisheye dataset
available.
