论文标题
外观的车辆属性识别：车辆类型，品牌和模型分类的计算机视觉方法
Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification
论文作者
论文摘要
该论文通过外观研究车辆属性识别。在文献中，基于图像的目标识别已在许多用例中进行了广泛的研究，例如面部识别，但在车辆属性识别领域中却较少。我们调查了许多算法，这些算法识别从粗粒水平（车辆类型）到细粒水平（车辆制造和型号）的车辆特性。此外，我们讨论了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，我们为车辆属性识别设计了模拟的现实世界情景，并对两种方法进行了实验比较。
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.
