Design of Multi-Task Object Counting System Based on Attention Mechanism
This article proposes a deep neural network based on the attention mechanism for object counting,whose task is to accurately count the number of targets in the input image.The network model simultaneously introduces a multi-task learning approach,fusing multi-scale features to obtain density and attention feature maps for object counting.Firstly,a cross-feature pyramid network is used for feature extraction.Then,the extracted features are used for density and attention feature maps,which are cross-fused.Finally,through multi-task learning,the two output feature maps are element-wise operated to obtain an accurate density feature map.The network model of this paper is trained and tested on pedestrian detection datasets(ShanghaiTech)and multi-category action recognition datasets(UCF_CC_50).The experimental results show that introducing the attention mechanism in each branch can effectively improve the accuracy of the entire model's predictive results.
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