ExSample: Efficient Searches on Video Repositories through Adaptive Sampling
ExSample: Efficient Searches on Video Repositories through Adaptive Sampling
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ExSample：通过自适应采样对视频存储库进行高效搜索
DOI：
10.1109/icde53745.2022.00266
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Tim Kraska
中科院分区：
文献类型：
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作者：
Oscar Moll;F. Bastani;Sam Madden;M. Stonebraker;V. Gadepally;Tim Kraska
随着摄像机的部署成本越来越低，捕获和处理视频变得越来越普遍。与此同时，丰富的视频理解方法在过去十年中取得了很大进展。因此，许多组织现在拥有大量视频数据存储库，并在地图，导航，自动驾驶和其他领域应用。由于最先进的物体检测方法速度慢且昂贵，我们在这些积累的数据上处理甚至简单的ad-hoc物体搜索查询（“在dashcam视频中找到100个交通灯”）的能力远远落后于我们收集数据的能力。对于这些类型的查询，以降低的采样率处理视频是合理的默认策略;然而，理想的采样率取决于数据和查询。我们引入ExSample，一个低成本的框架，用于在未索引的视频上进行对象搜索，通过将采样帧的数量和位置适应于正在处理的特定数据和查询来快速处理搜索查询。ExSample在视频存储库中优先处理帧，以便处理集中在最有可能包含感兴趣对象的视频部分。它以类似于多臂强盗问题的方式进行搜索，其中每个臂对应于视频的一部分。在大型的真实数据集上，ExSample平均将处理时间缩短了1.9倍，比高效的随机采样基线缩短了6倍。此外，我们还展示了ExSample在基于代理分数的复杂、最先进的基线开始产生其第一个结果之前很久就找到了许多结果。
Capturing and processing video is increasingly common as cameras become cheaper to deploy. At the same time, rich video-understanding methods have progressed greatly in the last decade. As a result, many organizations now have massive repositories of video data, with applications in mapping, navigation, autonomous driving, and other areas. Because state-of-the-art object-detection methods are slow and expensive, our ability to process even simple ad-hoc object search queries (“find 100 traffic lights in dashcam video”) over this accumulated data lags far behind our ability to collect the data. Processing video at reduced sampling rates is a reasonable default strategy for these types of queries; however, the ideal sampling rate is both data and query dependent. We introduce ExSample, a low cost framework for object search over un-indexed video that quickly processes search queries by adapting the amount and location of sampled frames to the particular data and query being processed. ExSample prioritizes the processing of frames in a video repository so that processing is focused in portions of video that most likely contain objects of interest. It approaches searching in a similar way to a multi-arm bandit problem where each arm corresponds to a portion of a video. On large, real-world datasets, ExSample reduces processing time by 1.9x on average and up to 6x over an efficient random sampling baseline. Moreover, we show ExSample finds many results long before sophisticated, state-of-the-art baselines based on proxy scores can begin producing their first results.
DOI：
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发表时间：
2018-01
期刊：
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影响因子：
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作者：
Kevin Hsieh;Ganesh Ananthanarayanan;P. Bodík;P. Bahl;Matthai Philipose;Phillip B. Gibbons;O. Mutlu
通讯作者：
Kevin Hsieh;Ganesh Ananthanarayanan;P. Bodík;P. Bahl;Matthai Philipose;Phillip B. Gibbons;O. Mutlu