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.

中文翻译：

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用于环视摄像头系统的车辆 Re-ID

车辆重新识别（ReID）在自动驾驶的感知系统中起着至关重要的作用，近年来受到越来越多的关注。然而，据我们所知，目前还没有安装在车辆上的环视系统的完整解决方案。在本文中，我们讨论了上述场景中的两个主要挑战：i) 在单相机视图中，由于鱼眼失真、遮挡、截断等，很难从过去的图像帧中识别同一辆车。 ii) 在多视图中摄像头视角，同一辆车的外观在不同摄像头的视角下差别很大。因此，我们提出了一个完整的车辆 Re-ID 解决方案来解决这些问题。具体来说，我们提出了一种新的质量评估机制来平衡跟踪框漂移和目标一致性的影响。除了，我们利用基于注意力机制的 Re-ID 网络，然后结合空间约束策略进一步提升不同相机之间的性能。实验表明，我们的解决方案实现了最先进的准确性，同时在实践中是实时的。此外，为了社区的利益，我们将发布代码和带注释的鱼眼数据集。