Abstract
Vehicle re-identification (V-reID) has become significantly popular in the community due to its applications and research significance. In particular, the V-reID is an important problem that still faces numerous open challenges. This paper reviews different V-reID methods including sensor based methods, hybrid methods, and vision based methods which are further categorized into hand-crafted feature based methods and deep feature based methods. The vision based methods make the V-reID problem particularly interesting, and our review systematically addresses and evaluates these methods for the first time. We conduct experiments on four comprehensive benchmark datasets and compare the performances of recent hand-crafted feature based methods and deep feature based methods. We present the detail analysis of these methods in terms of mean average precision (mAP) and cumulative matching curve (CMC). These analyses provide objective insight into the strengths and weaknesses of these methods. We also provide the details of different V-reID datasets and critically discuss the challenges and future trends of V-reID methods.
Abstract (translated)
车辆再识别（V-REID）由于其应用和研究意义，在社会上得到了广泛的应用。特别是，V-REID是一个仍然面临许多开放挑战的重要问题。本文综述了不同的V-REID方法，包括基于传感器的方法、混合方法和基于视觉的方法，并将这些方法进一步分为手工制作的基于特征的方法和基于深度特征的方法。基于视觉的方法使V-REID问题特别有趣，我们的综述首次系统地解决和评估了这些方法。我们在四个全面的基准数据集上进行了实验，并比较了最近手工制作的基于特征的方法和基于深度特征的方法的性能。本文从平均精度（MAP）和累积匹配曲线（CMC）两个方面对这些方法进行了详细的分析。这些分析提供了客观的洞察这些方法的优缺点。我们还提供了不同V-REID数据集的详细信息，并对V-REID方法的挑战和未来趋势进行了批判性讨论。
URL
https://arxiv.org/abs/1905.13258
