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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Learning a Part-Based Pedestrian Detector in a Virtual World
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Learning a Part-Based Pedestrian Detector in a Virtual World

机译：在虚拟世界中学习基于零件的行人检测器

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摘要

Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last 15 years. Among them, the so-called (deformable) part-based classifiers, including multiview modeling, are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, we first perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework, which also allows incorporating real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board data sets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent support vector machine.
机译：借助车载视觉系统检测行人对于协助驾驶员预防车辆与行人的事故至关重要。行人检测器的核心是其分类模块，该模块旨在确定给定的图像窗口是否包含行人。鉴于此任务的难度，在过去的15年中提出了许多分类器。其中，所谓的（可变形的）基于零件的分类器（包括多视图建模）通常在准确性方面排名最高。训练此类分类器并非易事，因为行人训练样本的正确方面聚类和空间部分对齐对于获得准确的分类器至关重要。在本文中，我们首先通过使用虚拟世界的行人执行自动方面聚类和零件对齐，即不需要人工注释。其次，我们使用零件混合方法，允许在不同方面之间共享零件。第三，将这些建议集成在一个学习框架中，该框架还允许合并真实世界的训练数据，以在虚拟相机和真实世界相机之间进行领域适应。总体而言，在四个流行的机载数据集上获得的结果表明，我们的建议明显优于被称为潜在支持向量机的最新的基于可变形部分的检测器。

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