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.

翻译：车辆再识别(Re-ID)是自动驾驶感知系统的一个关键组成部分,近年来这方面的研究加速了,然而,对于与汽车环形摄像系统有关的车辆再识别问题,还没有完全的解决办法。我们的分析在上述情景中确定了两个重要问题:一)由于鱼眼照相机的独特构造,很难在许多图象框中识别同一车辆。二)通过环形系统的若干摄像头看到同一车辆的外观相当不同。为了克服这些问题,我们建议采用一种综合车辆再识别解决方案。一方面,我们提供一种技术,以确定跟踪盒漂移与目标的一致性。另一方面,我们把基于关注机制的再识别网络与空间限制结合起来,以提高多照相机的性能。最后,我们的方法将最新技术的准确性与实时性能结合起来。我们将很快提供源代码和附加注释的鱼眼数据集。