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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Physical Features and Deep Learning-based Appearance Features for Vehicle Classification from Rear View Videos
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Physical Features and Deep Learning-based Appearance Features for Vehicle Classification from Rear View Videos

机译：从后视视频中进行车辆分类的物理特征和基于深度学习的外观特征

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摘要

Currently, there are many approaches for vehicle classification, but there is no specific study on automated, rear view, and video-based robust vehicle classification. The rear view is important for intelligent transportation systems since not all states in the United States require a frontal license plate on a vehicle. The classification of vehicles, from their rear views, is challenging since vehicles have only subtle appearance differences and there are changing illumination conditions and the presence of moving shadows. In this paper, we present a novel multi-class vehicle classification system that classifies a vehicle into one of four possible classes (sedan, minivan, SUV, and a pickup truck) from its rear view video, using physical and visual features. For a given geometric setup of the camera on highways, we make physical measurements on a vehicle. These measurements include visual rear ground clearance, the height of the vehicle, and the distance between the license plate and the rear bumper. We call these distances as the physical features. The visual features, also called appearance-based features, are extracted using convolutional neural networks from the input images. We achieve a classification accuracy of 93.22% and 91.52% using physical and visual features, respectively. Furthermore, we achieve a higher classification accuracy of 94.81% by fusing both the features together. The results are shown on a dataset consisting of 1831 rear view videos of vehicles and they are compared with various approaches, including deep learning techniques.
机译：当前，有许多用于车辆分类的方法，但是没有针对自动，后视和基于视频的鲁棒性车辆分类的具体研究。后视图对于智能交通系统非常重要，因为并非在美国所有州都需要在车辆上安装正面牌照。从车辆的后方看，车辆的分类具有挑战性，因为车辆的外观只有细微的差别，并且照明条件不断变化，并且存在运动阴影。在本文中，我们提出了一种新颖的多类别车辆分类系统，该系统利用其后视视频使用物理和视觉特征将车辆分为四个可能的类别（轿车，小型货车，SUV和皮卡车）中的一种。对于公路上摄像机的给定几何设置，我们在车辆上进行物理测量。这些测量包括视觉后方离地间隙，车辆高度以及牌照和后保险杠之间的距离。我们称这些距离为物理特征。使用卷积神经网络从输入图像中提取视觉特征（也称为基于外观的特征）。使用物理和视觉功能，我们分别实现93.22％和91.52％的分类精度。此外，通过将两个功能融合在一起，我们可以实现94.81％的更高分类精度。结果显示在由1831个车辆后视视频组成的数据集上，并将它们与包括深度学习技术在内的各种方法进行了比较。

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