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231225s2019 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2019.2928634
|2 doi
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|a DE-627
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|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Sindagi, Vishwanath A
|e verfasserin
|4 aut
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| 245 |
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|a HA-CCN
|b Hierarchical Attention-based Crowd Counting Network
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|c 2019
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Single image-based crowd counting has recently witnessed increased focus, but many leading methods are far from optimal, especially in highly congested scenes. In this paper, we present Hierarchical Attention-based Crowd Counting Network (HA-CCN) that employs attention mechanisms at various levels to selectively enhance the features of the network. The proposed method, which is based on the VGG16 network, consists of a spatial attention module (SAM) and a set of global attention modules (GAM). SAM enhances low-level features in the network by infusing spatial segmentation information, whereas the GAM focuses on enhancing channel-wise information in the higher level layers. The proposed method is a single-step training framework, simple to implement and achieves state-of-the-art results on different datasets. Furthermore, we extend the proposed counting network by introducing a novel set-up to adapt the network to different scenes and datasets via weak supervision using image-level labels. This new set up reduces the burden of acquiring labour intensive point-wise annotations for new datasets while improving the cross-dataset performance
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|a Journal Article
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|a Patel, Vishal M
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2019) vom: 19. Juli
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|x 1941-0042
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|g year:2019
|g day:19
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|u http://dx.doi.org/10.1109/TIP.2019.2928634
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