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Dual convolutional neural network for crowd counting
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AI
DOI：10.1007/s11042-023-16442-2.png)
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Abstract
En 中文
As a challenging issue in computer vision, crowd counting has been increasingly studied. A convolutional neural network (CNN) is an effective system for handling crowd counting, based on constructing a CNN to generate a high-quality density estimation map. However, conventional CNN-based methods only consider the mapping from the crowd image to the density map, neglecting reconstruction from the density map to the crowd image and the impact of this reconstruction on the CNN performance. Here, we present a novel model denoted a dual-CNN (DualCNN) to improve the conventional CNN performance on crowd counting. Our DualCNN comprises a primal network for generating the density maps from the crowd image and a secondary network for reconstructing the crowd image from the density map. The two networks are trained through an iterative and alternating learning process, and the performance of the final model is improved by considering the interactions of the two networks. In addition, we introduce the attention mechanism into the dual network to enhance the primal network robustness against the background influence of the crowd image. The experimental results indicate that the proposed method significantly improves the performance of CNNs in crowd counting.
Keywords:
Crowd counting
Dual network
Convolutional neural network
Journal
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3
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1.9W
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