- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | LI Hengchao, LIU Xianglian, LIU Peng, FENG Bin. Dense Crowd Counting Network Based on Multi-scale Perception[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1176-1183, 1214. doi: 10.3969/j.issn.0258-2724.20220823
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A dense crowd counting network based on multi-scale perception was proposed to solve the problems of diverse target scales and large-scale changes of crowds in dense crowd scenes. Firstly, since the small-scale targets account for a relatively large proportion of the images, a dilated convolution module was introduced based on the visual geometry group 2016 (VGG-16) network to mine the detailed information in the images. Then, by utilizing the multi-scale information of the target, a novel context-aware module was designed to extract the contrast features between different scales. Finally, In view of the continuous change of target scales, the multi-scale feature aggregation module was designed to improve the sampling range of dense scales, enhance the interaction of multi-scale information, and thus improve the model performance. The experimental results show that mean absolute errors (MAEs) of the proposed method are 62.5, 6.9, and 156.5, and the root mean square errors (RMSEs) are 95.7, 11.0, and 223.3 on ShangHai Tech (Part_A/Part_B) and UCF_CC_50 datasets, respectively. Compared with the optimal method of comparison model, the MAE and RMSE are reduced by 1.1% and 4.3% on the UCF_QNRF dataset and by 8.7% and 13.9% on the NWPU dataset.
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