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Multipart Vehicle Detection Using Symmetry-Derived Analysis and Active Learning

机译：基于对称性分析和主动学习的多部分车辆检测

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摘要

On-road vehicle detection is a critical operation in automotive active safety systems such as collision avoidance, merge assist, lane change assistance, etc. In this paper, we present VeDAS—Vehicle Detection using Active learning and Symmetry. VeDAS is a multipart-based vehicle detection algorithm that employs Haar-like features and Adaboost classifiers for the detection of fully and partially visible rear views of vehicles. In order to train the classifiers, a modified active learning framework is proposed that selects positive and negative samples of multiple parts in an automated manner. Furthermore, the detected parts from the classifiers are associated by using a novel iterative window search algorithm and a symmetry-based regression model to extract fully visible vehicles. The proposed method is evaluated on seven different datasets that capture varying road, traffic, and weather conditions. Detailed evaluations show that the proposed method gives high true positive rates of over 95% and performs better than existing state-of-the-art rear-view-based vehicle detection methods. Additionally, VeDAS also detects partially visible rear views of vehicles using the residues left behind after detecting the fully visible vehicles. VeDAS is able to detect partial rear views with a detection rate of 87% on a new partially visible rear-view vehicle dataset that we release as part of this paper.
机译：道路车辆检测是汽车主动安全系统中的关键操作，例如避免碰撞，合并辅助，变道辅助等。在本文中，我们介绍了VeDAS-使用主动学习和对称性的车辆检测。 VeDAS是一种基于多部分的车辆检测算法，该算法采用类似Haar的特征和Adaboost分类器来检测车辆的全部和部分可见的后视图。为了训练分类器，提出了一种改进的主动学习框架，该框架以自动化方式选择多个部分的正样本和负样本。此外，通过使用新颖的迭代窗口搜索算法和基于对称性的回归模型来提取分类器中检测到的零件，从而提取出完全可见的车辆。在七个捕获不同道路，交通和天气情况的不同数据集上对提出的方法进行了评估。详细的评估表明，所提出的方法具有超过95％的高真实阳性率，并且比现有的基于后视技术的车辆检测方法表现更好。此外，VeDAS还使用检测到完全可见的车辆后留下的残留物来检测车辆的部分可见的后视图。 VeDAS能够在我们作为本文一部分发布的新的部分可见的后视车辆数据集上以87％的检测率检测部分后视。

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