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Register a new userFocus: Querying Large Video Datasets with Low Latency and Low Cost
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Added by
Kevin Hsieh
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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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.
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Astronomy is well recognized as big data driven science. As the novel observation infrastructures are developed, the sky survey cycles have been shortened from a few days to a few seconds, causing data processing pressure to shift from offline to online. However, existing scientific databases focus on offline analysis of long-term historical data, not real-time and low latency analysis of large-scale newly arriving data. In this paper, a cloud based method is proposed to efficiently analyze scientific events on large-scale newly arriving data. The solution is implemented as a highly efficient system, namely Aserv. A set of compact data store and index structures are proposed to describe the proposed scientific events and a typical analysis pattern is formulized as a set of query operations. Domain aware filter, accuracy aware data partition, highly efficient index and frequently used statistical data designs are four key methods to optimize the performance of Aserv. Experimental results under the typical cloud environment show that the presented optimization mechanism can meet the low latency demand for both large data insertion and scientific event analysis. Aserv can insert 3.5 million rows of data within 3 seconds and perform the heaviest query on 6.7 billion rows of data also within 3 seconds. Furthermore, a performance model is given to help Aserv choose the right cloud resource setup to meet the guaranteed real-time performance requirement.
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