Crowd density estimation and warning model in key control areas of large factories based on CNN

Yang Fan; Zhu Wenjie; Zhao Rongyong

doi:10.1117/12.2634635

4 May 2022 Crowd density estimation and warning model in key control areas of large factories based on CNN

Yang Fan, Zhu Wenjie, Zhao Rongyong

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 121721B (2022) https://doi.org/10.1117/12.2634635
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

To meet to the requirements of the national production safety law of China, large manufacturing enterprises need to implement the sampling of dynamic number of employees in key areas, emergency evacuation drill and emergency evacuation organization. Aiming at the existing problems such as high randomness of overtime work of workshop employees, frequent worker aggregation phenomena for short meetings, fast aggregation, short time and high risk of dining employees in the canteens, uneven personnel distribution and crowded entrances and exits in the conference center, this paper analyzes the psychological and moving behavior characteristics of personnel in these typical scenarios, and introduces the convolution neural network (CNN) model in the field of machine vision, to build a multi-scenario employee number statistics and density estimation model for factory workshops, canteens and large meeting centers. Further, according to the density-risk relationship, a crowd aggregation risk early warning model is established. Finally, taking the video surveillance system (VSS) as the data acquisition source, the application cases of practical scenes such as workshop, canteen and meeting center are designed to verify the effectiveness of the density estimation model and aggregation risk early warning model proposed in this paper. Thereby this paper provides technical guarantee for the safety of employees in large manufacturing enterprises.

Citation Download Citation

Yang Fan, Zhu Wenjie, and Zhao Rongyong "Crowd density estimation and warning model in key control areas of large factories based on CNN", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 121721B (4 May 2022); https://doi.org/10.1117/12.2634635

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KEYWORDS

Convolution

Neural networks

Visual process modeling

Safety

Data modeling

Product safety

Control systems

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