Human Short Long-Term Cognitive Memory Mechanism for Visual Monitoring in IoT-Assisted Smart Cities

被引:41
作者
Wang, Shuai [1 ,2 ]
Liu, Xinyu [1 ,2 ]
Liu, Shuai [1 ,2 ]
Muhammad, Khan [3 ]
Heidari, Ali Asghar [4 ,5 ]
Del Ser, Javier [6 ,7 ]
de Albuquerque, Victor Hugo C. [8 ,9 ]
机构
[1] Hunan Normal Univ, Hunan Prov Key Lab Intelligent Comp & Language In, Coll Informat Sci & Engn, Changsha 410000, Peoples R China
[2] Hunan Normal Univ, Xiangjiang Coll Artificial Intelligence, Changsha 410000, Peoples R China
[3] Sungkyunkwan Univ, Coll Comp & Informat, Sch Convergence, Visual Analyt Knowledge Lab,VIS2KNOW Lab, Seoul 03063, South Korea
[4] Univ Tehran, Coll Engn, Sch Surveying & Geospatial Engn, Tehran 1439957131, Iran
[5] Natl Univ Singapore, Sch Comp, Dept Comp Sci, Singapore 119077, Singapore
[6] TECNALIA, Basque Res Technol Alliance BRTA, Derio 48160, Spain
[7] Univ Basque Country, Dept Commun Engn, UPV EHU, Bilbao 48013, Spain
[8] Univ Fed Ceara, Grad Program Teleinformat Engn, BR-60455970 Fortaleza, Ceara, Brazil
[9] Fed Inst Educ Sci & Technol Ceara, Grad Program Telecommun Engn, BR-60455970 Fortaleza, Ceara, Brazil
来源
IEEE INTERNET OF THINGS JOURNAL | 2022年 / 9卷 / 10期
关键词
Monitoring; Visualization; Training; Smart cities; Electronic mail; Correlation; Internet of Things; Filtering algorithms; IoT; long-term memory; short-term memory; smart city; tracking; OBJECT TRACKING; NEURAL-NETWORK; TECHNOLOGIES; 5G;
D O I
10.1109/JIOT.2021.3077600
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the industry 4.0 era, the visualization and real-time automatic monitoring of smart cities supported by the Internet of Things is becoming increasingly important. The use of filtering algorithms in smart city monitoring is a feasible method for this purpose. However, maintaining fast and accurate monitoring in complex surveillance environments with restricted resources remains a major challenge. Since the cognitive theory in visual monitoring is difficult to realize in practice, efficient monitoring of complex environments is accordingly hard to be achieved. Moreover, current monitoring methods do not consider the particularities of the human cognitive system, so the remonitoring ability of the process/target is weak in case of monitoring failure by the monitoring system. To overcome these issues, this article proposes a novel human short-long cognitive memory mechanism for video surveillance in smart cities. In this mechanism, a memory with a high reliability target is used as a "long-term memory," whereas a memory with a low reliability target is used as a "short-term memory." During the monitoring process, the "short-term memory" and "long-term memory" alternation strategy is combined with the stored target appearance characteristics, ensuring that the original model in the memory will not be contaminated or mislaid by changes in the external environment (occlusion, fast motion, motion blur, and background clutter). Extensive simulations showcase that the algorithm proposed in this article not only improves the monitoring speed without hindering its real-time operation but also monitors and traces the monitored target accurately, ultimately improving the robustness of the detection in complex scenery, and enabling its application to IoT-assisted smart cities.
引用
收藏
页码:7128 / 7139
页数:12
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