文献类型：期刊文献

英文题名：Light-weight AI and IoT collaboration for surveillance video pre-processing

作者：Liu, Yutong[1];Kong, Linghe[2];Chen, Guihai[2];Xu, Fangqin[3];Wang, Zhanquan[4]

机构：[1]Shanghai Jiao Tong Univ, Comp Sci, Shanghai, Peoples R China;[2]Shanghai Jiao Tong Univ, Shanghai, Peoples R China;[3]Shanghai Jianqiao Univ, Shanghai, Peoples R China;[4]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China

年份：2021

卷号：114

外文期刊名：JOURNAL OF SYSTEMS ARCHITECTURE

收录：;EI(收录号：20204809540458);WOS:【SCI-EXPANDED(收录号:WOS:000697350100009)】；

基金：This work was supported in part by National Key R&D Program of China 2018YFB1004703, NSFC, China grant 61972253, 61672349, U190820096, 61672348, 61672353, the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, China.

语种：英文

外文关键词：Light-weight AI; IoT collaboration; Wireless surveillance system; Dynamic background modelling; Edge computing

摘要：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.