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Active learning for on-road vehicle detection: a comparative study

机译：主动学习的道路车辆检测：一项比较研究

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摘要

In recent years, active learning has emerged as a powerful tool in building robust systems for object detection using computer vision. Indeed, active learning approaches to on-road vehicle detection have achieved impressive results. While active learning approaches for object detection have been explored and presented in the literature, few studies have been performed to comparatively assess costs and merits. In this study, we provide a cost-sensitive analysis of three popular active learning methods for on-road vehicle detection. The generality of active learning findings is demonstrated via learning experiments performed with detectors based on histogram of oriented gradient features and SVM classification (HOG-SVM), and Haar-like features and Adaboost classification (Haar-Adaboost). Experimental evaluation has been performed on static images and real-world on-road vehicle datasets. Learning approaches are assessed in terms of the time spent annotating, data required, recall, and precision.
机译：近年来，主动学习已成为构建使用计算机视觉进行对象检测的强大系统的强大工具。的确，用于道路车辆检测的主动学习方法已经取得了令人瞩目的成果。尽管已经探索并提出了用于对象检测的主动学习方法，但是很少进行研究来比较评估成本和优点。在这项研究中，我们对三种流行的主动学习方法进行成本敏感的分析，用于道路车辆检测。通过基于定向梯度特征和SVM分类（HOG-SVM）的直方图以及类似Haar的特征和Adaboost分类（Haar-Adaboost）的检测器执行的学习实验，可以证明主动学习发现的普遍性。已经对静态图像和真实世界的公路车辆数据集进行了实验评估。根据注释所花费的时间，所需的数据，回忆和准确性来评估学习方法。

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