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- Title
Hierarchical feature aggregation network with semantic attention for counting large‐scale crowd.
- Authors
Meng, Chen; Kang, Chunmeng; Lyu, Lei
- Abstract
The purpose of crowd counting is to estimate the number of people in an image. Due to the unconstrained imaging conditions, the scale variation and background occlusion in the images make it still challenging to achieve counting accurately. To tackle the two key challenges, we design a Hierarchical Feature Aggregation Network (HFANet) for accurate crowd counting in complex scenarios. The proposed method can extract multiple features at different levels and then aggregate them hierarchically to generate a high‐quality density map. To highlight the crowd regions effectively from the cluttered background, we propose the Semantic Attention module to preserve the useful feature information through the attention mechanism for the low‐level features extracted by VGG‐16. Meanwhile, we employ convolutional kernels of different sizes to extract multi‐scale features and global average pooling operations to preserve contextual information. Furthermore, we design the Feature Aggregation module to integrate the extracted multiple features through a progressive approach, which aims to take full advantage of the complementary properties between low‐level and high‐level features for efficient features aggregation. Finally, we evaluate the performance of the proposed HFANet on four challenging datasets. Extensive experimental results demonstrate that our proposed approach has better performance compared with most state‐of‐the‐art approaches.
- Subjects
CROWDS; COUNTING; MACHINE learning; COMPUTER vision; FEATURE extraction; OCCLUSION (Chemistry)
- Publication
International Journal of Intelligent Systems, 2022, Vol 37, Issue 11, p9957
- ISSN
0884-8173
- Publication type
Article
- DOI
10.1002/int.23023