全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于双镜头视野协同成像的无线视频传感器网络构建
Construction of Wireless Video Sensor Network Based on Dual Lens Field of View Collaborative Imaging

DOI: 10.12677/JSTA.2024.121007, PP. 54-62

Keywords: 视频监控,无线传感器网络,人工复眼,水利领域应用
Video Surveillance
, Wireless Sensor Network, Artificial Compound Eye, Application in the Field of Water Conservancy

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对地处边远地区江河湖库超大水域观测、大坝工程瞭望、恶劣天气条件下水面漂浮物检测、异常事件辨识及水体水质视觉监测等水利领域智能视频监控特有的应用需求,通过改进无线视频传感器网络(WVSN)、仿“复眼”成像以及视频边缘计算等现有的技术方法,本文提出一种基于双镜头视野协同成像的无线视频传感器网络(简称:WVSN-CI)设计新模式,为广域大范围的全域性水利智能视频监控系统建设提供前端网络设计解决方案。本文还对双镜头摄像机节点组成的WVSN-CI关键技术做出介绍,包括介绍和分析人工复眼与常规CMOS图像传感器双镜头合成,双镜(头)三核(芯)摄像机节点设备的光–机–电–算一体化设计,WVSN-CI视频数据传输协议,基于视频边缘计算的汇聚节点,以及WVSN-CI与水利信息化基础网络设施和视频主控中心的链接等内容。
Aiming at the special application needs of intelligent video surveillance in the water conservancy field, such as observation of super large waters of rivers, lakes and reservoirs in remote areas, observation of dam projects, detection of floating objects on the water surface under bad weather conditions, identification of abnormal events and visual monitoring of water quality, by improving existing technical methods such as wireless video sensor network (WVSN), imitation compound eye imaging and video edge computing, this paper proposes a new design pattern for wireless video sensor networks based on dual lens field of view collaborative imaging (referred to as WVSN-CI), that the pattern can provide an front-end network design solutions for the construction of wide area and wide range intelligent video surveillance system in water conservancy. The paper also introduces and analyzes the key technologies of the WVSN-CI composed of dual lens camera nodes, including dual lens synthesis of artificial compound eye and conventional CMOS image sensors, the integrated design of optical, mechanical, electrical, and computing of “dual mirror -three core” camera node equipment, video data transmission protocol for WVSN-CI, sink node based on video edge computing, and links between WVSN-CI and water conservancy information infrastructure, video control center, etc.

References

[1]  湖南贝哲斯信息咨询有限公司. 2023年全球与中国人工智能视频监控行业前景预测报告[R].
https://www.shangyexinzhi.com/article/9990378.html, 2023-07-18.
[2]  曹行健, 张志涛, 孙彦赞, 王平, 徐树公, 刘富强, 王超, 彭飞, 穆世义, 刘文予, 杨铀. 面向智慧交通的图像处理与边缘计算[J]. 中国图象图形学报, 2022, 27(6): 1743-1767.
[3]  程德强, 钱建生, 郭星歌, 寇旗旗, 徐飞翔, 顾军, 高亚超, 赵金升. 煤矿安全生产视频AI识别关键技术研究综述[J]. 煤炭科学技术, 2023, 51(2): 349-365.
[4]  李连国, 王丹华, 徐梦溪, 谭德宝, 文雄飞, 任康. 一种簇首选举优化与多跳机制结合的路由通信算法[J]. 计算机科学与应用, 2022, 12(7): 1801-1813.
[5]  梁伯虎, 夏颖, 张劲松. 无线网络视频监控系统的研究[J]. 信息系统工程, 2023(4): 25-27.
[6]  徐梦溪, 施建强, 王丹华. 天地网一体的水环境监测数据整合关键技术[J]. 水利信息化, 2021(2): 29-33.
[7]  徐梦溪, 施建强. 仿生复眼型多源监测数据融合与专题信息提取[J]. 水利信息化, 2021(1): 71-76.
[8]  杨铮, 贺骁武, 吴家行, 等. 面向实时视频流分析的边缘计算技术[J]. 中国科学(信息科学), 2022(52): 1-53.
[9]  Cheng, Y., Cao, J., Zhang, Y. and Hao, Q. (2019) Review of State-of-the-Art Artificial Compound Eye Imaging Systems. Bioinspi-ration & Biomimetics, 14, Article ID: 031002.
https://doi.org/10.1088/1748-3190/aaffb5
[10]  Xu, M., Wu, X., Zhang, Z. and Lu, Y. (2021) Compound-Eye Imaging Imitation-Based Whole-Field Flow Measurement. Computers and Electrical Engineering, 92, Article ID: 107141.
https://doi.org/10.1016/j.compeleceng.2021.107141
[11]  Fu, Q., Wang, H., Hu, C. and Yue, S. (2019) Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review. Artificial Life, 25, 263-311.
https://doi.org/10.1162/artl_a_00297
[12]  Neriec, N. and Desplan, C. (2016) From the Eye to the Brain: Development of the Drosophila Visual System. Current Topics in Developmental Biology, 116, 247-271.
https://doi.org/10.1016/bs.ctdb.2015.11.032
[13]  Wang, H., Peng, J. and Yue, S. (2020) A Directionally Selective Small Target Motion Detecting Visual Neural Network in Cluttered Backgrounds. IEEE Transactions on Cybernetics, 50, 1541-1555.
https://doi.org/10.1109/TCYB.2018.2869384
[14]  李柯, 沈克永, 刘宝, 曹阳, 邱晓健, 陈俊宇. 模拟飞虫复眼视觉的小目标运动检测与跟踪系统研究[J]. 图像与信号处理, 2022, 11(3): 92-100.
[15]  樊飞燕, 徐梦溪, 施建强, 陈谣. 基于人工复眼的红外目标运动检测研究[J]. 图像与信号处理, 2023, 12(2): 96-103.
[16]  施建强, 徐扬, 徐梦溪, 郑胜男. 一种用于感知目标运动方向的人工苍蝇视觉神经网络模型[C]//中国仪器仪表学会, 2021论文集(上册)中国仪器仪表学会学术年会. 上海: 中国仪器仪表学会, 2021: 1-2.
[17]  Jedari, B., Premsankar, G., Illahi, G., et al. (2021) Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions. IEEE Com-munications Surveys and Tutorials, 23, 431-471.
https://doi.org/10.1109/COMST.2020.3035427
[18]  曾婷, 黄东军. 智能视频监控系统异常行为检测算法研究综述[J]. 计算机测量与控制, 2021, 29(7): 1-6.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413