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AKITA: Application Knowledge Interface To Algorithms

机译：AKITA：算法的应用知识接口

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摘要

We propose a methodology for using sensor metadata and targeted preprocessing to optimize which selection from a large suite of algorithms are most appropriate for a given data set. Rather than applying several general purpose algorithms or requiring a human operator to oversee the analysis of the data, our method allows the most effective algorithm to be automatically chosen, conserving both computational, network and human resources. For example, the amount of video data being produced daily is far greater than can ever be analyzed. Computer vision algorithms can help sift for the relevant data, but not every algorithm is suited to every data type nor is it efficient to run them all. A full body detector won't work well when the camera is zoomed in or when it is raining and all the people are occluded by foul weather gear. However, leveraging metadata knowledge of the camera settings and the conditions under which the data was collected (generated by automatic preprocessing), face or umbrella detectors could be applied instead, increasing the likelihood of a correct reading. The Lockheed Martin AKITA™ system is a modular knowledge layer which uses knowledge of the system and environment to determine how to most efficiently and usefully process whatever data it is given.
机译：我们提出了一种使用传感器元数据和目标预处理的方法，以优化从大量算法中选择哪个最适合给定数据集。我们的方法无需应用几种通用算法或要求操作员来监督数据分析，而是可以自动选择最有效的算法，从而节省了计算，网络和人力资源。例如，每天产生的视频数据量远远超过了可以分析的量。计算机视觉算法可以帮助筛选相关数据，但是并非每种算法都适用于每种数据类型，也不是有效地运行所有算法。当相机放大或下雨并且所有人都被恶劣天气的装备遮挡时，全身检测器将无法正常工作。但是，利用相机设置的元数据知识和收集数据的条件（通过自动预处理生成），可以改用面部或雨伞检测器，从而增加了正确读取的可能性。洛克希德·马丁公司的AKITA™系统是模块化的知识层，它利用对系统和环境的了解来确定如何最有效地处理给定的数据。

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