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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >ISEE: An Intelligent Scene Exploration and Evaluation Platform for Large-Scale Visual Surveillance
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ISEE: An Intelligent Scene Exploration and Evaluation Platform for Large-Scale Visual Surveillance

机译：ISEE：用于大规模视觉监控的智能场景探索和评估平台

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摘要

Intelligent video surveillance (IVS) is always an interesting research topic to utilize visual analysis algorithms for exploring richly structured information from big surveillance data. However, existing IVS systems either struggle to utilize computing resources adequately to improve the efficiency of large-scale video analysis, or present a customized system for specific video analytic functions. It still lacks of a comprehensive computing architecture to enhance efficiency, extensibility and flexibility of IVS system. Moreover, it is also an open problem to study the effect of the combinations of multiple vision modules on the final performance of end applications of IVS system. Motivated by these challenges, we develop an Intelligent Scene Exploration and Evaluation (ISEE) platform based on a heterogeneous CPU-GPU cluster and some distributed computing tools, where Spark Streaming serves as the computing engine for efficient large-scale video processing and Kafka is adopted as a middle-ware message center to decouple different analysis modules flexibly. To validate the efficiency of the ISEE and study the evaluation problem on composable systems, we instantiate the ISEE for an end application on person retrieval with three visual analysis modules, including pedestrian detection with tracking, attribute recognition and re-identification. Extensive experiments are performed on a large-scale surveillance video dataset involving 25 camera scenes, totally 587 hours 720p synchronous videos, where a two-stage question-answering procedure is proposed to measure the performance of execution pipelines composed of multiple visual analysis algorithms based on millions of attribute-based and relationship-based queries. The case study of system-level evaluations may inspire researchers to improve visual analysis algorithms and combining strategies from the view of a scalable and composable system in the future.
机译：智能视频监视（IVS）一直是一个有趣的研究主题，利用视觉分析算法从大型监视数据中探索结构丰富的信息。但是，现有的IVS系统要么难以充分利用计算资源来提高大规模视频分析的效率，要么会提供针对特定视频分析功能的定制系统。它仍然缺乏完善的计算架构来提高IVS系统的效率，可扩展性和灵活性。而且，研究多个视觉模块的组合对IVS系统最终应用的最终性能的影响也是一个开放的问题。受这些挑战的驱使，我们开发了基于异构CPU-GPU集群和一些分布式计算工具的智能场景探索和评估（ISEE）平台，其中Spark Streaming用作高效大规模视频处理的计算引擎，并采用了Kafka作为中间件消息中心，可以灵活地分离不同的分析模块。为了验证ISEE的效率并研究可组合系统的评估问题，我们使用三个可视化分析模块实例化了用于人员检索的最终应用程序的ISEE，这三个可视化分析模块包括具有跟踪功能的行人检测，属性识别和重新识别。在涉及25个摄像机场景，总共587小时720p同步视频的大规模监控视频数据集上进行了广泛的实验，其中提出了一个两阶段的问答程序来测量基于多种视觉分析算法的执行流水线的性能。数百万个基于属性和基于关系的查询。系统级评估的案例研究可能会激发研究人员改进视觉分析算法，并从未来可扩展和可组合的系统的角度结合策略。

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