Active Vision Dataset Benchmark
Active Vision Dataset Benchmark
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DOI：
10.1109/cvprw.2018.00277
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Phil Ammirato;A. Berg;J. Kosecka
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文献类型：
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作者：
Phil Ammirato;A. Berg;J. Kosecka
最近在计算机视觉方面的几项努力表明，在更大规模的环境中研究和理解问题的趋势，超越了单一的图像，并专注于导航，移动的操作和视觉问题回答中的任务的连接。这些任务的一个共同目标是在环境中移动的能力，在感知期间和执行任务时获得新的视图。这种能力在合成环境中很容易实现，但是用真实的图像实现相同的效果要费力得多。我们建议使用现有的主动视觉数据集，形成一个基准，在现实世界的设置与真实的图像等问题。该数据集非常适合于评估多视图主动识别，目标驱动导航和目标搜索的任务，也可以有效地研究在模拟中学习到的策略转移到真实的设置。
Several recent efforts in computer vision indicate a trend toward studying and understanding problems in larger scale environments, beyond single images, and focus on connections to tasks in navigation, mobile manipulation, and visual question answering. A common goal of these tasks is the capability of moving in the environment, acquiring novel views during perception and while performing a task. This capability comes easily in synthetic environments, however achieving the same effect with real images is much more laborious. We propose using the existing Active Vision Dataset to form a benchmark for such problems in a real-world settings with real images. The dataset is well suited for evaluating tasks of multiview active recognition, target driven navigation, and target search, and also can be effective for studying the transfer of strategies learned in simulation to real settings.