Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things

被引：16
作者：
Danesh Pazho A. [1 ]
Neff C. [1 ]
Noghre G.A. [1 ]
Ardabili B.R. [1 ]
Yao S. [1 ]
Baharani M. [1 ]
Tabkhi H. [1 ]
机构：
[1] The University of North Carolina at Charlotte, Electrical and Computer Engineering Department, Charlotte, 28223, NC
来源：
IEEE Internet of Things Journal | 2023年 / 10卷 / 17期
关键词：
Anomaly; application; artificial intelligence; computer vision; edge; Internet of Things (IoT); real time; real world; surveillance;
D O I：
10.1109/JIOT.2023.3263725
中图分类号：
学科分类号：
摘要：
With the advancement of vision-based artificial intelligence, the proliferation of the Internet of Things connected cameras, and the increasing societal need for rapid and equitable security, the demand for accurate real-Time intelligent surveillance has never been higher. This article presents Ancilia, an end-To-end scalable, intelligent video surveillance system for the Artificial Intelligence of Things. Ancilia brings state-of-The-Art artificial intelligence to real-world surveillance applications while respecting ethical concerns and performing high-level cognitive tasks in real time. Ancilia aims to revolutionize the surveillance landscape, to bring more effective, intelligent, and equitable security to the field, resulting in safer and more secure communities without requiring people to compromise their right to privacy. © 2014 IEEE.
引用
收藏
页码：14940 / 14951
页数：11
相关论文
共 69 条
  • [1] Alexandre C., The public safety implications of the Itaewon tragedy
  • [2] Salahieh N., Miller J., Yan H., As north Carolinians regain power, investigators probe terrorism and threats against power substations across the us, One expert explains what needs to be done
  • [3] Feldstein S., The global expansion of AI surveillance, Carnegie Endowment for International Peace, (2019)
  • [4] Patrikar M.R., Parate D.R., Anomaly detection using edge computing in video surveillance system: Review, Int. J. Multimedia Inf. Retrieval, 11, pp. 85-110
  • [5] Pazho A.D., Noghre G.A., Purkayastha A.A., Vempati J., Martin O., Tabkhi H., A comprehensive survey of graph-based deep learning approaches for anomaly detection in complex distributed systems, 2022
  • [6] Li X., Cai Z.-M., Anomaly detection techniques in surveillance videos, Proc. 9th Int. Congr. Image Signal Process. BioMed. Eng. Inform. (CISP-BMEI), pp. 54-59, (2016)
  • [7] Li T., Chang H., Wang M., Ni B., Hong R., Yan S., Crowded scene analysis: A survey, IEEE Trans. Circuits Syst. Video Technol., 25, 3, pp. 367-386, (2015)
  • [8] Shobha B.S., Deepu R., A review on video based vehicle detection, recognition and tracking, Proc. 3rd Int. Conf. Comput. Syst. Inf. Technol. Sustain. Solutions (CSITSS), pp. 183-186, (2018)
  • [9] Zhang J., Xu C., Gao Z., Rodrigues J.J.P.C., de Albuquerque V.H.C., Industrial pervasive edge computing-based intelligence IoT for surveillance saliency detection, IEEE Trans. Ind. Informat., 17, 7, pp. 5012-5020
  • [10] Draughon G.T.S., Sun P., Lynch J.P., Implementation of a computer vision framework for tracking and visualizing face mask usage in urban environments, Proc. IEEE Int. Smart Cities Conf. (ISC2), 2020, pp. 1-8
1234567