Crowd counting with segmentation attention convolutional neural network
This addresses crowd counting for surveillance and public safety, but it is incremental as it builds on existing deep learning approaches with attention mechanisms.
The paper tackles crowd counting by proposing SegCrowdNet, a CNN architecture that uses segmentation attention to highlight human head regions and suppress non-head areas, achieving excellent performance compared to state-of-the-art methods on four challenging datasets.
Deep learning occupies an undisputed dominance in crowd counting. In this paper, we propose a novel convolutional neural network (CNN) architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposeSegCrowdNet still adaptively highlights the human head region and suppresses the non-head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. The crowd count can be obtained by integrating the density map. To adapt the variation of crowd counts, SegCrowdNet intelligently classifies the crowd count of each image into several groups. In addition, the multi-scale features are learned and extracted in the proposed SegCrowdNet to overcome the scale variations of the crowd. To verify the effectiveness of our proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that our proposed SegCrowdNet achieves excellent performance compared with the state-of-the-art methods.