@inproceedings{e3cd265f552a42b7a5047e4bc2fe5e92,
title = "Multi-feature counting of dense crowd image based on multi-column convolutional neural network",
abstract = "The crowd counting task is an important research problem. Now more and more people are concerned about safety issues. When the population density reaches a very high peak, the population density counts, the alarm is sent out, and the crowds are diverted. The trampling of the Shanghai New Year's stampede will not happen again. The final density map is produced by two steps: at first, extract feature maps from multiple layers, and then adjust their output so that they are all the same size, all these resized layers are combined into the final density map. We also used texture features and target edge detection to reduce the loss of density map detail to better integrate with our convolutional neural network. We tested on several commonly used datasets. Our model achieved good results in crowd counting.",
keywords = "CNN, Texture features",
author = "Songchenchen Gong and Bourennane, {El Bay} and Junyu Gao",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 5th International Conference on Computer and Communication Systems, ICCCS 2020 ; Conference date: 15-05-2020 Through 18-05-2020",
year = "2020",
month = may,
doi = "10.1109/ICCCS49078.2020.9118564",
language = "英语",
series = "2020 5th International Conference on Computer and Communication Systems, ICCCS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "215--219",
booktitle = "2020 5th International Conference on Computer and Communication Systems, ICCCS 2020",
}