Multi Layered Deep Neural Network for Feature Extraction in Cross Domain Crowd Counting
2022 International Conference on Decision Aid Sciences and Applications, DASA 2022
; : 1051-1056, 2022.
Article
in English
| Scopus | ID: covidwho-1872067
ABSTRACT
Automated crowd density monitoring is an emerging area of research. It is a vital technology that assists during recent disease outbreaks in preserving social distancing, crowd management and other widespread applications in public security and traffic control. Modern methods to count people in crowded scenes mainly rely on Convolutional Neural Network (CNN) based models. But the model's ability to adapt for different domains which is referred to as cross domain crowd counting is a challenging task. To remedy this difficulty, many researchers used Spatial Fully Convolutional Network (SFCN) based crowd counting models with synthetic crowd scene data. They covered many image domains with few-shot learning to reduce the domain adaptation gap between source and target image domains. In this paper, we propose a new multi-layered model architecture instead of SFCN single-layered model architecture. The proposed model extracts more meaningful features in image scenes along with large scale variations to increase the accuracy in cross domain crowd counting. Furthermore, with extensive experiments using four real-world datasets and analysis, we show that the proposed multi-layered architecture performs well with synthetic image data and few-shot learning in reducing domain shifts. © 2022 IEEE.
cross domain crowd counting; crowd density estimation; domain adaptation; few-shot learning; Computer vision; Convolution; Convolutional neural networks; Disease control; Multilayer neural networks; Network architecture; Network layers; Convolutional networks; Cross-domain; Crowd density; Density estimation; Image domain; Multi-layered; Deep neural networks
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Randomized controlled trials
Language:
English
Journal:
2022 International Conference on Decision Aid Sciences and Applications, DASA 2022
Year:
2022
Document Type:
Article
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