Abstract：With the continuous development of computer vision and artificial intelligence, crowd counting algorithms based on intelligent video analysis have made considerable headway. However, the counting accuracy and robustness are far from satisfactory. Aiming at the problem of multi-scale feature and background interference in crowd counting task, an anti-background interference crowd counting network based on multi-scale feature fusion(AntiNet-MFF) is proposed. Based on the U-Net network architecture, a hierarchical feature split block is integrated into the AntiNet-MFF model, and multi-scale features of the crowd are also extracted with the help of the powerful representation capability of deep learning. To increase the attention of the counting model to the crowd area and reduce the interference of background noise, a background segmentation attention map(B-Seg Attention Map) is generated in the decoding stage. Then, B-Seg attention map is taken as the attention to guide counting model in focusing on the head area to improve the quality of the crowd distribution density map. Experiments on several typical crowd counting datasets show that AntiNet-MFF achieves promising results in terms of accuracy and robustness compared with the existing algorithms.
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2022, Vol. 35 
