Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144965
Title: Multi-resolution attention convolutional neural network for crowd counting
Authors: Zhang, Youmei
Zhou, Chunluan
Chang, Faliang
Kot, Alex Chichung
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Zhang, Y., Zhou, C., Chang, F., & Kot, A. C. (2019). Multi-resolution attention convolutional neural network for crowd counting. Neurocomputing, 329, 144–152. doi:10.1016/j.neucom.2018.10.058
Journal: Neurocomputing
Abstract: Estimating crowd counts remains a challenging task due to the problems of scale variations, non-uniform distribution and complex backgrounds. In this paper, we propose a multi-resolution attention convolutional neural network (MRA-CNN) to address this challenging task. Except for the counting task, we exploit an additional density-level classification task during training and combine features learned for the two tasks, thus forming multi-scale, multi-contextual features to cope with the scale variation and non-uniform distribution. Besides, we utilize a multi-resolution attention (MRA) model to generate score maps, where head locations are with higher scores to guide the network to focus on head regions and suppress non-head regions regardless of the complex backgrounds. During the generation of score maps, atrous convolution layers are used to expand the receptive field with fewer parameters, thus getting higher-level features and providing the MRA model more comprehensive information. Experiments on ShanghaiTech, WorldExpo’10 and UCF datasets demonstrate the effectiveness of our method.
URI: https://hdl.handle.net/10356/144965
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2018.10.058
Rights: © 2018 Elsevier B.V. All rights reserved. This paper was published in Neurocomputing and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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