Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151340
Title: Atrous convolutions spatial pyramid network for crowd counting and density estimation
Authors: Ma, Junjie
Dai, Yaping
Tan, Yap Peng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Ma, J., Dai, Y. & Tan, Y. P. (2019). Atrous convolutions spatial pyramid network for crowd counting and density estimation. Neurocomputing, 350, 91-101. https://dx.doi.org/10.1016/j.neucom.2019.03.065
Journal: Neurocomputing
Abstract: Scale variation because of perspective distortion is still a challenge for crowd analysis. To address this problem, an atrous convolutions spatial pyramid network (ACSPNet) is proposed to perform crowd counts and density maps for both sparse and congested scenarios. Atrous Convolutions sequenced with increasing atrous rates are utilized to exaggerate the receptive field and maintain the resolution of extracted features. Different rates of atrous convolution blocks in the pyramid are skip-connected to integrate multi-scale information and extent scale perception ability. Atrous Spatial Pyramid Pooling (ASPP) is employed to resample information at different scales and contain global context. We evaluate our ACSPNet on five challenging benchmark crowd counting datasets and our method achieves state-of-the-art mean absolute error (MAE) and mean squared error (MSE) performances.
URI: https://hdl.handle.net/10356/151340
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.03.065
Rights: © 2019 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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