Density map refinement algorithm based on adaptive strategy
In crowd counting tasks,density maps generated from Gaussian kernel-smoothed point graphs are typically used as intermediate training products.However,comparative experiments on different density label generation methods have revealed a significant discrepancy between generated labels and actual scenarios.Despite ongoing improvements in density map generation,the resulting training gains are limited.Current research focuses primarily on optimizing network structures and loss functions,yet overlooks the correction of density map biases.Inspired by self-adaptive training algorithms,an adaptive density map correction al-gorithm is designed,which dynamically calibrates the distribution of generated density maps,reducing bias in ground-truth through model predictions.This method can be integrated into nearly all crowd counting models based on Convolutional Neural Networks(CNNs)or self-attention models(Transformers),substantially enhancing model accuracy and robustness.Experimental results on various crowd datasets demonstrate that models incorporating this approach achieve higher accuracy and enhanced robustness.
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