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A Large and Diverse Dataset for Improved Vehicle Make and Model Recognition

机译：用于改进车辆的大型和多样化数据集和模型识别

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摘要

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS) and corresponding components such as Automated Vehicular Surveillance (AVS). A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multiclass classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicle makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system.,,,,,, In this paper, facing the growing importance of make and model recognition of vehicles, we present an image dataset1 with 9; 170 different classes of vehicles to advance the corresponding tasks. Extensive experiments conducted using baseline approaches yield superior results for images that were occluded, under low illumination, partial or nonfrontal camera views, available in our VMMR dataset. The approaches presented herewith provide a robust VMMR system for applications in realistic environments.
机译：由于其在许多智能运输系统（其）和自动车载监测（AVS）等相应的组件中，车辆制作和模型识别（VMMR）已经发展成为一种重要的研究主题。一种高度准确和实时的VMMR系统显着降低了否则所需资源的开销成本。 VMMR问题是多种多组分类任务，其各种车辆制作和模型之间的多种问题和挑战等挑战，可以以高效可靠的方式解决，以实现高度稳健的VMMR系统。,,,,,，本文面对越来越重要的制造和模型识别车辆，我们呈现了9个图像数据集1; 170种不同的车辆推进相应的任务。使用基线进行的广泛实验方法，在我们的VMMR数据集中提供的低照明，部分或非野蛮相机视图下，可以在低照明，部分或非野蛮相机视图下产生卓越的结果。这里介绍的方法为现实环境中的应用提供了一种强大的VMMR系统。

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