Crowd counting with crowd attention convolutional neural network
- URL: http://arxiv.org/abs/2204.07347v1
- Date: Fri, 15 Apr 2022 06:51:58 GMT
- Title: Crowd counting with crowd attention convolutional neural network
- Authors: Jiwei Chen, Wen Su, Zengfu Wang
- Abstract summary: We propose a novel end-to-end model called Crowd Attention Convolutional Neural Network (CAT-CNN)
Our CAT-CNN can adaptively assess the importance of a human head at each pixel location by automatically encoding a confidence map.
With the guidance of the confidence map, the position of human head in estimated density map gets more attention to encode the final density map, which can avoid enormous misjudgements effectively.
- Score: 23.96936386014949
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Crowd counting is a challenging problem due to the scene complexity and scale
variation. Although deep learning has achieved great improvement in crowd
counting, scene complexity affects the judgement of these methods and they
usually regard some objects as people mistakenly; causing potentially enormous
errors in the crowd counting result. To address the problem, we propose a novel
end-to-end model called Crowd Attention Convolutional Neural Network (CAT-CNN).
Our CAT-CNN can adaptively assess the importance of a human head at each pixel
location by automatically encoding a confidence map. With the guidance of the
confidence map, the position of human head in estimated density map gets more
attention to encode the final density map, which can avoid enormous
misjudgements effectively. The crowd count can be obtained by integrating the
final density map. To encode a highly refined density map, the total crowd
count of each image is classified in a designed classification task and we
first explicitly map the prior of the population-level category to feature
maps. To verify the efficiency of our proposed method, extensive experiments
are conducted on three highly challenging datasets. Results establish the
superiority of our method over many state-of-the-art methods.
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