Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask. (24th August 2022)

• Record Type: Journal Article
• Authors: Ren, Guoyin; Lu, Xiaoqi; Wang, Jingyu; Li, Yuhao
• Other Names: Iqbal Nadeem Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-08-24
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/5708807
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Background . In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate. Methods . In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN "mid-term" fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection. Results . MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%. Conclusion . MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets.