Implementation of intrusion detection system in CUDA for real-time multi-node streaming

S. M. Tahir, O. Shen, Lee Chin Yang, E. Karuppiah
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引用次数: 3

Abstract

A common surveillance activity is to track important people, or people exhibiting suspicious behavior, as they move from one camera surveillance area to another. The reduction in video hardware cost has made it more feasible for large scale camera deployment. However, the increased scale of camera deployment creates difficulties for humans to track people through the monitored space and to recognize important events as they happen in timely manner without human intervention. In this paper we share the implementation of the multi node video analytics specifically focusing on intrusion detection. The system uses general purpose graphical processing unit (GPGPU) to offload the video analytics processing. The architecture of the GPGPU requires the algorithm to be coded in Compute Unified Device Architecture (CUDA) which involves algorithm parallelization adopting both micro and macro parallelization to ensure the performance gain in processing speed on per frame basis by 7 times. In addition, we have managed to deploy 35 camera streams on single GPU card running at 20 frames per second which results in scalability factor of 1.75 times vs. a server class PC. Indeed, we have also managed to maintain the video analytics accuracy at 100% for given test dataset, in this implementation of the system.
基于CUDA的实时多节点流入侵检测系统实现
一种常见的监视活动是跟踪重要人物或表现出可疑行为的人,因为他们从一个摄像头监控区域移动到另一个摄像头监控区域。视频硬件成本的降低使得大规模摄像机部署更加可行。然而,摄像头部署规模的增加给人类在监控空间中跟踪人员以及在没有人为干预的情况下及时识别重要事件带来了困难。在本文中,我们分享了多节点视频分析的实现,特别是针对入侵检测。该系统采用通用图形处理单元GPGPU来卸载视频分析处理。GPGPU的架构要求算法在CUDA (Compute Unified Device architecture)中编码,CUDA涉及算法并行化,采用宏微并行化,保证每帧处理速度的性能提升7倍。此外,我们已经设法在单个GPU卡上部署35个摄像机流,以每秒20帧的速度运行,这使得可扩展性系数是服务器级PC的1.75倍。事实上,在这个系统的实现中,我们还设法将给定测试数据集的视频分析准确性保持在100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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