Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (6): 6-12.doi: 10.16180/j.cnki.issn1007-7820.2022.06.002

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Crowd Counting Algorithm Based on Residual Dense Connection and Attention Fusion

SHEN Ningjing,YUAN Jian   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
  • Received:2021-01-28 Online:2022-06-15 Published:2022-06-20
  • Supported by:
    National Natural Science Foundation of China(61775139)


The existing crowd counting algorithm uses multi-column fusion structure to solve the multi-scale problem of a single image, which cannot effectively use the low-level feature information, resulting in inaccurate final crowd counting results. In order to improve the accuracy, a crowd counting algorithm based on residual dense connection and attention fusion is proposed. The algorithm uses improved VGG16 network to extract low-level feature information. Based on the residual dense connection structure, the back-end main branch of the proposed algorithm uses the combination of residual network and dense network to capture the feature information between layers and efficiently capture multi-scale information. Side branch introduces the attention mechanism to generate the corresponding scale attention map, which effectively distinguishes the background and prospect of the feature map and reduces the influence of background noise. The algorithm is tested on three mainstream public data sets. The experimental results show that the algorithm is effective in counting and has better counting accuracy than other algorithms.

Key words: crowd counting, dense residuals, attention, convolutional neural network, density figure, feature fusion, multi-scale, nearest neighbor interpolation

CLC Number: 

  • TP391