Crowd counting with segmentation attention convolutional neural network

Abstract

Deep learning occupies an undisputed dominance in crowd counting. This paper proposes a novel convolutional neural network architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposed SegCrowdNet 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 this proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that the proposed SegCrowdNet achieves excellent performance compared with the state‐of‐the‐art methods.

SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing

“…And it improves the accuracy of crowd counting dramatically. The CNN based methods [9], [16], [17] regress to the density map where the location and quantity of crowd are recorded. Due to the serious scale variation in the crowd, many methods [3], [9], [13], [18], [19] are proposed to overcome it.…”