使用目标检测的自动地点分类

P. Viswanathan, T. Southey, J. Little, Alan K. Mackworth
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引用次数: 17

摘要

环境中的位置可以通过它们所包含的对象来描述。本文讨论了在单一系统中实现目标检测和位置分类的完全自动化集成。我们首先从一个在线注释数据库中执行对象-地点关系的自动学习。然后我们在一些最常见的物体上训练物体检测器。最后,我们利用检测分数和学习到的物体-地点关系对图像进行地点分类。我们还讨论了需要改进的领域以及这项工作在知情视觉搜索中的应用。作为一个整体,该系统演示了包含标记实例(即边界框)的训练数据的自动获取,以及在此数据上训练的最先进的物体检测技术的性能,以执行真实室内场景的位置分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Place Classification Using Object Detection
Places in an environment can be described by the objects they contain. This paper discusses the completely automated integration of object detection and place classification in a single system. We first perform automated learning of object-place relations from an online annotated database. We then train object detectors on some of the most frequently occurring objects. Finally, we use detection scores as well as learned object-place relations to perform place classification of images. We also discuss areas for improvement and the application of this work to informed visual search. As a whole, the system demonstrates the automated acquisition of training data containing labeled instances (i.e. bounding boxes) and the performance of a state-of-the-art object detection technique trained on this data to perform place classification of realistic indoor scenes.
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