Object detection and classification are the basic tasks in video analytics and become the starting point for other complex applications. Traditional video analytics approaches are manual and time consuming. These are subjective due to the very involvement of human factor. We present a cloud based video analytics framework for scalable and robust analysis of video streams. The framework empowers an operator by automating the object detection and classification process from recorded video streams. An operator only specifies an analysis criteria and duration of video streams to analyse. The streams are then fetched from a cloud storage, decoded and analysed on the cloud. The framework executes compute intensive parts of the analysis to GPU powered servers in the cloud. Vehicle and face detection are presented as two case studies for evaluating the framework, with one month of data and a 15 node cloud. The framework reliably performed object detection and classification on the data, comprising of 21,600 video streams and 175 GB in size, in 6.52 hours. The GPU enabled deployment of the framework took 3 hours to perform analysis on the same number of video streams, thus making it at least twice as fast than the cloud deployment without GPUs.

中文翻译：

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云中的视频流分析：用于高性能视频分析的对象检测和分类框架

对象检测和分类是视频分析的基本任务，并成为其他复杂应用程序的起点。传统的视频分析方法是手动且耗时的。由于人为因素的参与，这些是主观的。我们提出了一个基于云的视频分析框架，用于对视频流进行可扩展和稳健的分析。该框架通过从录制的视频流中自动化对象检测和分类过程来为操作员赋能。运营商仅指定要分析的视频流的分析标准和时长。然后从云存储中获取流，在云上进行解码和分析。该框架对云中 GPU 供电的服务器执行分析的计算密集型部分。车辆和人脸检测作为评估框架的两个案例研究提供，具有一个月的数据和 15 个节点的云。该框架在 6.52 小时内可靠地对数据执行了对象检测和分类，包括 21,600 个视频流和 175 GB 的大小。支持 GPU 的框架部署需要 3 小时才能对相同数量的视频流进行分析，因此其速度至少比没有 GPU 的云部署快两倍。