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Title	Crowd Counting By Using Top-K Relations: A Mixed Ground-Truth Cnn Framework
ID_Doc	16657
Authors	Dong L.; Zhang H.; Yang K.; Zhou D.; Shi J.; Ma J.
Year	2022
Published	IEEE Transactions on Consumer Electronics, 68, 3
DOI	http://dx.doi.org/10.1109/TCE.2022.3190384
Abstract	Crowd counting has important applications in the environments of smart cities, such as intelligent surveillance. In this paper, we propose a novel convolutional neural network (CNN) framework for crowd counting with mixed ground-truth, called top- k relation-based network (TKRNet). Specifically, the estimated density maps generated in a coarse-to-fine manner are treated as coarse locations for crowds so as to assist our TKRNet to regress the scattered point-annotated ground truth. Moreover, an adaptive top- k relation module (ATRM) is proposed to enhance feature representations by leveraging the top- k dependencies between the pixels with an adaptive filtering mechanism. Specifically, we first compute the similarity between two pixels so as to select the top- k relations for each position. Then, a weight normalization operation with an adaptive filtering mechanism is proposed to make the ATRM adaptively eliminate the influence from the low correlation positions in the top- k relations. Finally, a weight attention mechanism is introduced to make the ATRM pay more attention to the positions with high weights in the top- k relations. Extensive experimental results demonstrate the effectiveness of our proposed TKRNet on several public datasets in comparison to state-of-the-art methods. © 1975-2011 IEEE.
Author Keywords	Crowd counting; mixed ground truth; self-attention; top-k relations

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