Next Article in Journal
Unsupervised Clustering of Hyperspectral Paper Data Using t-SNE
Previous Article in Journal
Fusing Appearance and Spatio-Temporal Models for Person Re-Identification and Tracking
Open AccessArticle

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

1
School of Information and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand
2
School of Information Science, Japan Advanced Institute of Science and Technology, Ishikawa 923-1211, Japan
3
National Electronic and Computer Technology Center, National Science and Technology Development Agency, Pathum Thani 12120, Thailand
*
Author to whom correspondence should be addressed.
J. Imaging 2020, 6(5), 28; https://doi.org/10.3390/jimaging6050028
Received: 18 March 2020 / Revised: 27 April 2020 / Accepted: 29 April 2020 / Published: 2 May 2020
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. View Full-Text
Keywords: surveillance system; crowd counting; regression-based approach; skip connection; dilated convolution surveillance system; crowd counting; regression-based approach; skip connection; dilated convolution
Show Figures

Figure 1

MDPI and ACS Style

Sooksatra, S.; Kondo, T.; Bunnun, P.; Yoshitaka, A. Redesigned Skip-Network for Crowd Counting with Dilated Convolution and Backward Connection. J. Imaging 2020, 6, 28.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop