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Title: Cross-scale generative adversarial network for crowd density estimation from images
Authors: Zhang, Gaowei
Pan, Yue
Zhang, Limao
Tiong, Robert Lee Kong
Keywords: Engineering::Civil engineering
Issue Date: 2020
Source: Zhang, G., Pan, Y., Zhang, L. & Tiong, R. L. K. (2020). Cross-scale generative adversarial network for crowd density estimation from images. Engineering Applications of Artificial Intelligence, 94, 103777-.
Project: M4011971.030
Journal: Engineering Applications of Artificial Intelligence
Abstract: This research develops a cross-scale convolutional spatial generative adversarial network (CSGAN), in order to estimate the crowd density from images accurately. It consists of two similar generators, one for the whole feature extraction, and the other for patch scale feature extraction. An encoder–decoder structure is employed to generate density maps from input images or patches. Additionally, a new objective function for crowd counting called cross-scale consistency pursuit containing an adversarial loss, L2 loss, perceptual loss, and consistency loss, is developed to make the generated density maps more realistic and closer to the ground truth. The effectiveness of the proposed CSGAN is verified in two public datasets. Results indicate that the new objective function is able to reach the most satisfying value of evaluation metrics in both the low-density and high-density crowd scenes when it is compared with other state-of-the-art methods on the test datasets. Moreover, the proposed CSGAN is more practical and flexible due to the smaller computational complexity. Its estimation capability will be significantly improved even in a small size of training data. Overall, this research contributes to the development of a novel computer vision approach together with a new objective function to generate density maps from cross-scale crowd images, enabling the counting process more accurately and efficiently.
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2020.103777
Schools: School of Civil and Environmental Engineering 
Rights: © 2020 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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