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Automated Vehicle Detection and Classification: Models, Methods, and Techniques

机译：自动车辆检测和分类：模型，方法和技术

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摘要

Automated Vehicle Classification (AVC) based on vision sensors has received active attention from researchers, due to heightened security concerns in Intelligent Transportation Systems. In this work, we propose a categorization of AVC studies based on the granularity of classification, namely Vehicle Type Recognition, Vehicle Make Recognition, and Vehicle Make and Model Recognition. For each category of AVC systems, we present a comprehensive review and comparison of features extraction, global representation, and classification techniques. We also present the accuracy and speed-related performance metrics and discuss how they can be used to compare and evaluate different AVC works. The various datasets proposed over the years for AVC are also compared in light of the real-world challenges they represent, and those they do not. The major challenges involved in each category of AVC systems are presented, highlighting open problems in this area of research. Finally, we conclude by providing future directions of research in this area, paving the way toward efficient large-scale AVC systems. This survey shall help researchers interested in the area to analyze works completed so far in each category of AVC, focusing on techniques proposed for each module, and to chalk out strategies to enhance state-of-the-art technology.
机译：由于智能交通系统中对安全性的日益关注，基于视觉传感器的自动车辆分类（AVC）受到了研究人员的积极关注。在这项工作中，我们基于分类的粒度，即车辆类型识别，车辆制造识别以及车辆制造和模型识别，提出了AVC研究的分类。对于AVC系统的每个类别，我们都会对特征提取，全局表示和分类技术进行全面的回顾和比较。我们还将介绍与准确性和速度相关的性能指标，并讨论如何将它们用于比较和评估不同的AVC工作。还将针对多年来提出的AVC提出的各种数据集进行比较，并根据它们所代表的现实挑战以及不存在的挑战进行比较。提出了每类AVC系统涉及的主要挑战，突出了该研究领域中的未解决问题。最后，我们通过提供该领域的未来研究方向作为结论，为高效的大规模AVC系统铺平了道路。这项调查将帮助对该领域感兴趣的研究人员分析迄今为止在AVC的每个类别中完成的工作，重点研究针对每个模块提出的技术，并提出增强最新技术的策略。

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