标题:Intelligent monitoring of indoor surveillance video based on deep learning
作者:Liu, Yun-Xia; Yang, Yang; Shi, Aijun; Peng Jigang; Liu Haowei
通讯作者:Liu, YX;Liu, YX
作者机构:[Liu, Yun-Xia] Univ Jinan, Sch Informat Sci & Engn, Jinan 250022, Shandong, Peoples R China.; [Liu, Yun-Xia] Univ Jinan, Shandong Prov Key Lab Netwo 更多
会议名称:21st International Conference on Advanced Communication Technology (ICACT)
会议日期:FEB 17-20, 2019
来源:2019 21ST INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION TECHNOLOGY (ICACT): ICT FOR 4TH INDUSTRIAL REVOLUTION
出版年:2019
卷:2019-February
页码:648-653
DOI:10.23919/ICACT.2019.8701964
关键词:Surveillance Video; Deep Learning; Mask R-CNN; Object Detection;; Instance Segmentation
摘要:With the rapid development of information technology, video surveillance system has become a key part in the security and protection system of modern cities. Especially in prisons, surveillance cameras could be found almost everywhere. However, with the continuous expansion of the surveillance network, surveillance cameras not only bring convenience, but also produce a massive amount of monitoring data, which poses huge challenges to storage, analytics and retrieval. The smart monitoring system equipped with intelligent video analytics technology can monitor as well as pre-alarm abnormal events or behaviours, which is a hot research direction in the field of surveillance. This paper combines deep learning methods, using the state-of-the-art framework for instance segmentation, called Mask R-CNN, to train the fine-tuning network on our datasets, which can efficiently detect objects in a video image while simultaneously generating a high-quality segmentation mask for each instance. The experiment show that our network is simple to train and easy to generalize to other datasets, and the mask average precision is nearly up to 98.5% on our own datasets.
收录类别:CPCI-S;EI
资源类型:会议论文
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