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Density-aware and background-aware network for crowd counting via multi-task learning.
- Source :
-
Pattern Recognition Letters . Oct2021, Vol. 150, p221-227. 7p. - Publication Year :
- 2021
-
Abstract
- • We propose a density-aware and background-aware multi-task learning network for crowd counting. • A multi-task joint loss is proposed for our multi-task learning network. • Extensive experiments are conducted on three crowd counting benchmark datasets and promising results are achieved. In this paper, we propose a density-aware and background-aware network via multi-task learning (MTL-DB) for crowd counting. It aims to enable the model to capture the high-level semantic information of density and background via multi-task joint training, which may jointly optimize the generation of density maps. Initially, MTL-DB utilizes the first ten layers of VGG-16 with Batch Normalization as the front-end to extract primary features which will be shared by all tasks. Then, a multi-task back-end is constructed by integrating the main task of density map estimation with two auxiliary tasks, i.e., density classification and background segmentation. The density classification auxiliary task captures the density-related information with a fully connected classifier, while the background segmentation auxiliary task applies dilated convolutional network to distinguish the head area of pedestrians and background. With high-level semantic awareness, the main task generates estimated density maps utilizing normal convolutional layers. Furthermore, a multi-task joint loss is proposed to improve the quality of estimated density maps. Extensive experiments on three challenging crowd datasets (ShanghaiTech Part A & B, UCF_CC_50, and UCF_QNRF) verified the effectiveness of this multi-task learning model. MTL-DB outperformed other multi-task learning methods on the ShanghaiTech dataset, both Part A and Part B. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PEDESTRIANS
*PEDESTRIAN areas
*CROWDS
*COUNTING
Subjects
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 150
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 152272258
- Full Text :
- https://doi.org/10.1016/j.patrec.2021.07.013