Spatial-aware crowd counting algorithm based on dual discriminator strategy
Crowd counting is an important research field in computer vision.In order to accurately calculate the number of rows in static images or video frames,the regression density map method is usually used to improve the counting accuracy.However,due to the domain shift problem between the source domain and the target domain,the generalization ability of the model on the scarce label data set is insufficient.This paper proposes a space-aware spatial awareness of dual discriminator strategy(SADDS)domain adaptive crowd counting algorithm based on dual discriminator strategy,which aims to effectively solve the difference between image style and head scale.The algorithm combines hierarchical attention mechanism(HAM),including spatial attention mechanism(SAM)and global attention mechanism(GAM),in the feature extraction process of DSANET backbone network VGG16.At the same time,global and local double discriminators are used to reduce domain shift.The effectiveness and superiority of the method are verified by experiments on public data sets.
万方数据
维普
