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Light-weight AI and IoT collaboration for surveillance video pre-processing
Journal of Systems Architecture ( IF 4.5 ) Pub Date : 2020-11-18 , DOI: 10.1016/j.sysarc.2020.101934
Yutong Liu , Linghe Kong , Guihai Chen , Fangqin Xu , Zhanquan Wang

As one of the internet of things (IoT) use cases, wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. Videos captured by surveillance cameras are required to be uploaded for further storage and analysis, while the large amount of its raw data brings great challenges to the transmission through resource-constraint wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames before uploading can dramatically relieve the transmission pressure. Additionally, real-world monitoring environment may bring shielding or blind areas in videos, which notoriously affects the accuracy on frame filtering. The collaboration between neighbouring cameras can compensate for such accuracy loss.

Under the computational constraint of edge cameras, we present an efficient video pre-processing strategy for wireless surveillance systems using light-weight AI and IoT collaboration. Two main modules are designed for either fixed or rotated cameras: (i) frame filtering module by dynamic background modelling and light-weight deep learning analysis; and (ii) collaborative validation module for error compensation among neighbouring cameras. Evaluations based on real-collected videos show the efficiency of this strategy. It achieves 64.4% bandwidth saving for the static scenario and 61.1% for the dynamic scenario, compared with the raw video transmission. Remarkably, the relatively high balance ratio between frame filtering accuracy and latency overhead outperforms than state-of-the-art light-weight AI structures and other surveillance video processing methods, implying the feasibility of this strategy.



中文翻译:

轻量级AI和IoT协作以实现监控视频预处理

作为物联网(IoT)用例之一,无线监控系统由于易于部署和性能提高而迅速普及。监视摄像机捕获的视频需要上传以进行进一步的存储和分析,而其大量原始数据给通过资源受限的无线网络进行传输带来了巨大挑战。观察到大多数收集到的连续帧都是多余的,只有很少的关注对象(OoIs),在上传之前对这些帧进行过滤可以极大地减轻传输压力。此外,实际的监视环境可能会在视频中带来遮挡或盲区,这无疑会影响帧过滤的准确性。相邻摄像机之间的协作可以弥补这种精度损失。

在边缘摄像机的计算约束下,我们提出了一种使用轻量级AI和IoT协作的无线监控系统有效视频预处理策略。为固定或旋转相机设计了两个主要模块:(i)通过动态背景建模和轻型深度学习分析的帧过滤模块;(ii)协同验证模块,用于相邻摄像机之间的误差补偿。根据实际收集的视频进行的评估表明了该策略的有效性。与原始视频传输相比,静态场景的带宽节省为64.4%,动态场景的带宽节省为61.1%。值得注意的是

更新日期:2020-11-18
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