Relational Attention Network for Crowd Counting
Rattachement africain : cn, ae. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Crowd counting is receiving rapidly growing research interests due to its potential application value in numerous real-world scenarios. However, due to various challenges such as occlusion, insufficient resolution and dynamic backgrounds, crowd counting remains an unsolved problem in computer vision. Density estimation is a popular strategy for crowd counting, where conventional density estimation methods perform pixel-wise regression without explicitly accounting the interdependence of pixels. As a result, independent pixel-wise predictions can be noisy and inconsistent. In order to address such an issue, we propose a Relational Attention Network (RANet) with a self-attention mechanism for capturing interdependence of pixels. The RANet enhances the self-attention mechanism by accounting both short-range and long-range interdependence of pixels, where we respectively denote these implementations as local self-attention (LSA) and global self-attention (GSA). We further introduce a relation module to fuse LSA and GSA to achieve more informative aggregated feature representations. We conduct extensive experiments on four public datasets, including ShanghaiTech A, ShanghaiTech B, UCF-CC-50 and UCF-QNRF. Experimental results on all datasets suggest RANet consistently reduces estimation errors and surpasses the state-of-the-art approaches by large margins.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Relational Attention Network for Crowd Counting
- Date Crossref
- 01/10/2019
- Éditeur
- IEEE
- Type
- proceedings-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Beihang University Ministry of Industry and Information Technology of China pays non établi dans la noticeUniversité ou école supérieure
-
Inception Institute of Artificial Intelligence pays non établi dans la noticeStructure de recherche
-
Beijing Advanced Sciences and Innovation Center pays non établi dans la noticeStructure de recherche
-
Ministry of Industry and Information Technology pays non établi dans la noticeOrganisme public
-
School of Electronic and Information Engineering pays non établi dans la noticeUniversité ou école supérieure
-
Beijing Advanced Innovation Center for Big Data-Based Precision Medicine pays non établi dans la noticeInstitution
Ministry of Industry and Information Technology of China — Beihang University, Inception Institute of Artificial Intelligence et Beijing Advanced Sciences and Innovation Center, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.