Redesigned Skip-Network for Crowd Counting with Dilated Convolution and Backward Connection

Autor: Toshiaki Kondo, Atsuo Yoshitaka, Pished Bunnun, Sorn Sooksatra
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Mean squared error
Computer science
Mean absolute error
02 engineering and technology
010501 environmental sciences
lcsh:Computer applications to medicine. Medical informatics
01 natural sciences
surveillance system
Article
lcsh:QA75.5-76.95
Robustness (computer science)
0202 electrical engineering
electronic engineering
information engineering

crowd counting
Radiology
Nuclear Medicine and imaging

lcsh:Photography
Electrical and Electronic Engineering
Semantic information
Crowd counting
0105 earth and related environmental sciences
business.industry
Pattern recognition
dilated convolution
lcsh:TR1-1050
Computer Graphics and Computer-Aided Design
skip connection
regression-based approach
lcsh:R858-859.7
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
lcsh:Electronic computers. Computer science
Crowd density
business
Zdroj: Journal of Imaging, Vol 6, Iss 28, p 28 (2020)
Journal of Imaging
Volume 6
Issue 5
Popis: Crowd counting is a challenging task dealing with the variation of an object scale and a crowd density. Existing works have emphasized on skip connections by integrating shallower layers with deeper layers, where each layer extracts features in a different object scale and crowd density. However, only high-level features are emphasized while ignoring low-level features. This paper proposes an estimation network by passing high-level features to shallow layers and emphasizing its low-level feature. Since an estimation network is a hierarchical network, a high-level feature is also emphasized by an improved low-level feature. Our estimation network consists of two identical networks for extracting a high-level feature and estimating the final result. To preserve semantic information, dilated convolution is employed without resizing the feature map. Our method was tested in three datasets for counting humans and vehicles in a crowd image. The counting performance is evaluated by mean absolute error and root mean squared error indicating the accuracy and robustness of an estimation network, respectively. The experimental result shows that our network outperforms other related works in a high crowd density and is effective for reducing over-counting error in the overall case.
Databáze: OpenAIRE
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