• Laser & Optoelectronics Progress
  • Vol. 58, Issue 20, 2010021 (2021)
Meng Yuebo1、2, Chen Xuanrun1, Liu Guanghui1、*, and Xu Shengjun1、2
Author Affiliations
  • 1College of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China
  • 2Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, Guangdong 510000, China
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    Abstract

    Crowd density estimation has important application value in the field of intelligent security prevention. A crowd density estimation method with multi-feature information fusion is proposed to address the problems of large difference in viewpoint change of two-dimensional images, loss of feature spatial information, and difficulties in scale feature and crowd feature extraction. The proposed method encodes the multi-view information of images through the attention mechanism-guided perspective of spatial attention (PSA) method to obtain the spatial global contextual information of the feature map and weaken the influence of viewpoint change. Through the multi-scale information aggregation (MSIA) method, the multi-scale asymmetric convolution and the null convolution with different expansion rates are effectively integrated to obtain more comprehensive image scale and feature information. Finally, the spatial information of the high-level feature map and the semantic information of the low-level feature map are complemented by the detailed semantic feature embedding fusion, and the contextual information and scale information complement each other to improve the accuracy and robustness of the model. The experimental validation is carried out using the ShanghaiTech, Mall, and Worldexpo’10 datasets, and the experimental results show that the performance of the proposed method has been improved compared with those of other comparative methods.
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    Yuebo Meng, Xuanrun Chen, Guanghui Liu, Shengjun Xu. Crowd Density Estimation Method Based on Multi-Feature Information Fusion[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010021
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    Category: Image Processing
    Received: Mar. 4, 2021
    Accepted: Mar. 23, 2021
    Published Online: Oct. 14, 2021
    The Author Email: Liu Guanghui (guanghuil@163.com)