JACIII Vol.23 No.2 pp. 305-308
doi: 10.20965/jaciii.2019.p0305

Short Paper:

The Application of A-CNN in Crowd Counting of Scenic Spots

Wanli Luo and Jialiang Wang

College of Information and Engineering, Sichuan Tourism University
No. 459 Hongling Road, Longquanyi District, Chengdu, Sichuan 610000, China

Corresponding author

June 22, 2018
August 20, 2018
March 20, 2019
crowd counting, scenic spots, CNN, deep learning

In places where people are concentrated, such as scenic spots, the statistical accuracy of existing crowd statistics algorithms is not enough. In order to solve this problem, a crowd counting algorithm based on adaptive convolution neural network (A-CNN) is proposed, which is based on video monitoring technology. The process of its pooling is dynamically adjusted according to different feature graphs. Then the pooled weights are adjusted adaptively according to the contents of each pooled domain. Therefore, CNN can extract more accurate features when processing different pooled domains under different iteration times, so as to achieve adaptive effect finally. The experimental results show that the proposed A-CNN algorithm has improved the recognition accuracy.

The accurate crowd counting statistics in scenic spots

The accurate crowd counting statistics in scenic spots

Cite this article as:
W. Luo and J. Wang, “The Application of A-CNN in Crowd Counting of Scenic Spots,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.2, pp. 305-308, 2019.
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Last updated on Jun. 03, 2024