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Density-aware and background-aware network for crowd counting via multi-task learning
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DOI：10.1016/j.patrec.2021.07.013.png)
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Abstract
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In this paper, we propose a density-aware and background-aware network via multi-task learning (MTLDB) 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. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Crowd counting
Multi-task learning
Density map
Auxiliary task
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