Multi-scale convolutional neural networks for crowd counting
Lingke Zeng,Xiangmin Xu,Bolun Cai,Suo Qiu,Tong Zhang +4 more
- pp 465-469
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TLDR
A novel multi-scale convolutional neural network (MSCNN) for single image crowd counting is proposed, able to generate scale-relevant features for higher crowd counting performances in a single-column architecture, which is both accuracy and cost effective for practical applications.Abstract:
Crowd counting on static images is a challenging problem due to scale variations. Recently deep neural networks have been shown to be effective in this task. However, existing neural-networks-based methods often use the multi-column or multi-network model to extract the scale-relevant features, which is more complicated for optimization and computation wasting. To this end, we propose a novel multi-scale convolutional neural network (MSCNN) for single image crowd counting. Based on the multi-scale blobs, the network is able to generate scale-relevant features for higher crowd counting performances in a single-column architecture, which is both accuracy and cost effective for practical applications. Complemental results show that our method outperforms the state-of-the-art methods on both accuracy and robustness with far less number of parameters.read more
Citations
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Proceedings ArticleDOI
Learning From Synthetic Data for Crowd Counting in the Wild
TL;DR: A data collector and labeler is developed which can generate the synthetic crowd scenes and simultaneously annotate them without any manpower, and a crowd counting method via domain adaptation is proposed, which can free humans from heavy data annotations.
Proceedings ArticleDOI
Crowd Counting with Deep Negative Correlation Learning
TL;DR: This work proposes a new learning strategy to produce generalizable features by way of deep negative correlation learning (NCL), which deeply learn a pool of decorrelated regressors with sound generalization capabilities through managing their intrinsic diversities.
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Learning from Synthetic Data for Crowd Counting in the Wild
TL;DR: Wang et al. as discussed by the authors developed a data collector and labeler to generate the synthetic crowd scenes and simultaneously annotate them without any manpower, which can boost the performance of crowd counting in the wild.
Proceedings ArticleDOI
Crowd Counting With Deep Structured Scale Integration Network
TL;DR: Zhang et al. as discussed by the authors proposed a novel Deep Structured Scale Integration Network (DSSINet) for crowd counting, which addresses the scale variation of people by using structured feature representation learning and hierarchically structured loss function optimization.
Proceedings ArticleDOI
Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization
TL;DR: This work proposes Recurrent Attentive Zooming Network, which recurrently detects ambiguous image region and zooms it into high resolution for re-inspection and proposes an adaptive fusion scheme that effectively elevates the performance.
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