Optimizing Deep CNN-Based Queries over Video Streams at Scale

ABSTRACT

We show that the optimal set of filters and their parameters depends significantly on the video stream and query in question, so NOSCOPE introduces an efficient cost-based optimizer for this problem to select them. With this approach, our NOSCOPE prototype achieves up to 120-3,200× speed-ups (318- 8,500× real-time) on binary classification tasks over real-world webcam and surveillance video while maintaining accuracy within 1-5% of a state-of-the-art CNN.

1. INTRODUCTION

In this paper, we address this tension between CNNs’ accuracy and computational expense by employing one of the mainstays of data management system design: query optimization.

Just as conventional database engines automatically adapt their query processing plans to properties of the data [68], NOSCOPE automatically adapts CNN-based video query processing to the contents of video streams

正如传统的数据库引擎自动将其查询处理方案适应于数据[68],NOSCOPE自动将基于CNN的视频查询处理与视频流的内容相适应。

As a result, NOSCOPE allows efficient and accurate classification and label extraction of data streams—without requiring manual tuning.

In its search for equivalent but efficient CNN filters, NOSCOPE exploits a key property of video data: video content is often highly redundant. NOSCOPE employs two types of filters that exploit several types of locality (i.e., similarities) within a stream. First, video data exhibits temporal locality.  Second, video exhibits scene specific spatial locality.

The key challenge in exploiting the above observations is that the optimal configuration of filters is highly data-dependent. Diverse filters will perform very differently on different scenes, offering distinct tradeoffs between speed, selectivity, and accuracy.

To summarize, our contributions are:

1. NOSCOPE, the first data management system that accelerates CNN-based classification queries over video streams at scale.

2. CNN-specific techniques for difference detection across frames and model specialization for a given stream and query, as well as a cost-based optimizer that can automatically identify the best combination of these filters for a given accuracy target.

3. An evaluation of NOSCOPE on fixed-angle binary classification showing up to 3,200× speedups on real-world data.

 

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