Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation

• URL: http://arxiv.org/abs/2206.10075v1
• Date: Tue, 21 Jun 2022 02:05:41 GMT
• Title: Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation
• Authors: Yuehai Chen, Jing Yang, Badong Chen and Shaoyi Du
• Abstract summary: Human tend to locate and count the target in low-density regions, and reason the number in high-density regions. We propose a CNN and Transformer Adaptive Selection Network (CTASNet) which can adaptively select the appropriate counting branch for different density regions.
• Score: 25.050801798414263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real-world crowd counting applications, the crowd densities in an image vary greatly. When facing with density variation, human tend to locate and count the target in low-density regions, and reason the number in high-density regions. We observe that CNN focus on the local information correlation using a fixed-size convolution kernel and the Transformer could effectively extract the semantic crowd information by using the global self-attention mechanism. Thus, CNN could locate and estimate crowd accurately in low-density regions, while it is hard to properly perceive density in high-density regions. On the contrary, Transformer, has a high reliability in high-density regions, but fails to locate the target in sparse regions. Neither CNN or Transformer can well deal with this kind of density variations. To address this problem, we propose a CNN and Transformer Adaptive Selection Network (CTASNet) which can adaptively select the appropriate counting branch for different density regions. Firstly, CTASNet generates the prediction results of CNN and Transformer. Then, considering that CNN/Transformer are appropriate for low/high-density regions, a density guided Adaptive Selection Module is designed to automatically combine the predictions of CNN and Transformer. Moreover, to reduce the influences of annotation noise, we introduce a Correntropy based Optimal Transport loss. Extensive experiments on four challenging crowd counting datasets have validated the proposed method.

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