Focus: Querying Large Video Datasets with Low Latency and Low Cost
Focus: Querying Large Video Datasets with Low Latency and Low Cost
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发表时间：
2018-01
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通讯作者：
Kevin Hsieh;Ganesh Ananthanarayanan;P. Bodík;P. Bahl;Matthai Philipose;Phillip B. Gibbons;O. Mutlu
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作者：
Kevin Hsieh;Ganesh Ananthanarayanan;P. Bodík;P. Bahl;Matthai Philipose;Phillip B. Gibbons;O. Mutlu
从部署进行交通控制和监视的摄像机中连续记录大量视频，目的是回答“事实之后”查询：从录制视频的许多天数中识别带有某些类（汽车，袋子）对象的视频框架。虽然卷积神经网络（CNN）的进步已经使这些查询的准确性很高，但它们太昂贵且缓慢。我们建立了焦点，这是一个用于大型视频数据集上低延迟和低成本查询的系统。 Focus使用廉价的摄入技术将视频索引由其中发生的对象。在摄入时间，它使用CNN的压缩和特定于视频的专业化。 Focus通过明智地利用昂贵的CNN来处理廉价CNN的较低准确性。为了减少查询时间延迟，我们聚集了相似的对象，因此避免了冗余处理。使用来自流量，监视和新闻频道的视频流上的实验，我们看到焦点使用的GPU周期要比运行昂贵的摄入处理器少58倍，并且比在查询时间处理所有视频的速度要快37倍。
Large volumes of videos are continuously recorded from cameras deployed for traffic control and surveillance with the goal of answering "after the fact" queries: identify video frames with objects of certain classes (cars, bags) from many days of recorded video. While advancements in convolutional neural networks (CNNs) have enabled answering such queries with high accuracy, they are too expensive and slow. We build Focus, a system for low-latency and low-cost querying on large video datasets. Focus uses cheap ingestion techniques to index the videos by the objects occurring in them. At ingest-time, it uses compression and video-specific specialization of CNNs. Focus handles the lower accuracy of the cheap CNNs by judiciously leveraging expensive CNNs at query-time. To reduce query time latency, we cluster similar objects and hence avoid redundant processing. Using experiments on video streams from traffic, surveillance and news channels, we see that Focus uses 58X fewer GPU cycles than running expensive ingest processors and is 37X faster than processing all the video at query time.