This paper studies vehicle attribute recognition by appearance. In the literature, image-based target recognition has been extensively investigated in many use cases, such as facial recognition, but less so in the field of vehicle attribute recognition. We survey a number of algorithms that identify vehicle properties ranging from coarse-grained level (vehicle type) to fine-grained level (vehicle make and model). Moreover, we discuss two alternative approaches for these tasks, including straightforward classification and a more flexible metric learning method. Furthermore, we design a simulated real-world scenario for vehicle attribute recognition and present an experimental comparison of the two approaches.

本文研究了基于外观的车辆属性识别。在文献中，基于图像的目标识别已在许多用例中得到了广泛研究，例如面部识别，但在车辆属性识别领域却很少。我们调查了许多识别车辆性能的算法，从粗粒度级别（车辆类型）到细粒度级别（车辆品牌和型号）。此外，我们讨论了用于这些任务的两种替代方法，包括直接分类和更灵活的度量学习方法。此外，我们设计了用于车辆属性识别的模拟现实世界场景，并对两种方法进行了实验比较。