| Title |
An attentive hierarchy ConvNet for crowd counting in smart city |
| ID_Doc |
36070 |
| Authors |
Zhai, WZ; Gao, ML; Souri, A; Li, QL; Guo, XY; Shang, JR; Zou, GF |
| Title |
An attentive hierarchy ConvNet for crowd counting in smart city |
| Year |
2023 |
| Published |
Cluster Computing-The Journal Of Networks Software Tools And Applications, 26, 2 |
| DOI |
10.1007/s10586-022-03749-2 |
| Abstract |
Crowd counting plays a crucial rule in the development of smart city. However, the problems of scale variations and background interferences degrade the performance of the crowd counting in real-world scenarios. To address these problems, a novel attentive hierarchy ConvNet (AHNet) is proposed in this paper. The AHNet extracts hierarchy features by a designed discriminative feature extractor and mines the semantic features in a coarse-to-fine manner by a hierarchical fusion strategy. Meanwhile, a re-calibrated attention (RA) module is built in various levels to suppress the influence of background interferences, and a feature enhancement (FE) module is built to recognize head regions at various scales. Experimental results on five people crowd datasets and two cross-domain vehicle crowd datasets illustrate that the proposed AHNet achieves competitive performance in accuracy and generalization. |
| Author Keywords |
Smart city; Crowd counting; Attention mechanism; Hierarchical strategy |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000856609300001 |
| WoS Category |
Computer Science, Information Systems; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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