Research on deep learning recognition model of human behavior for financial institution monitoring based on RFID

Xuehan Hou; Mulan Yang; Lvqing Yang; Yan Song; Weihua Qian; Yishu Qiu

doi:10.1117/12.3004458

16 August 2023 Research on deep learning recognition model of human behavior for financial institution monitoring based on RFID

Xuehan Hou, Mulan Yang, Lvqing Yang, Yan Song, Weihua Qian, Yishu Qiu

Author Affiliations +

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 127871S (2023) https://doi.org/10.1117/12.3004458
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

With the continuous development of the national economy, people's demand for the security of financial institutionsiscontinually increasing. Compared with human monitoring, with the help of human behavior recognition technology, collecting data from sensors to identify the behavior of criminals has the advantages of fast data processing and lowcost. This paper takes the human behavior recognition project based on RFID and deep learning as the research object, andstudies how to effectively extract behavior features and judge human actions according to the value of features. Byoptimizing the convolutional neural network, the project has both good feature construction ability and memory abilitytoextract action marks. Hopefully, all-weather, fast and accurate response to illegal acts in self-service banks andother places can be provided, and the informatization of financial institutions can be promoted.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xuehan Hou, Mulan Yang, Lvqing Yang, Yan Song, Weihua Qian, and Yishu Qiu "Research on deep learning recognition model of human behavior for financial institution monitoring based on RFID", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 127871S (16 August 2023); https://doi.org/10.1117/12.3004458

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