基于残差密集连接与注意力融合的人群计数算法

doi:10.16180/j.cnki.issn1007-7820.2022.06.002

Abstract

Abstract:

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

SHEN Ningjing,YUAN Jian. Crowd Counting Algorithm Based on Residual Dense Connection and Attention Fusion[J].Electronic Science and Technology, 2022, 35(6): 6-12.

Figures/Tables 9

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References 24

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方法	PartA		PartB
方法	MAE	RMSE	MAE	RMSE
SwitchCNN^[9]	90.4	135.0	21.6	33.4
MSCNN^[10]	83.8	127.4	17.7	30.2
CSRNet^[12]	68.2	115.0	10.6	16.0
MDNet^[13]	66.9	108.3	9.2	15.1
RDCAF	65.6	108.0	8.51	14.2

方法	MAE	RMSE
Ridge Regression^[21]	3.59	19.0
Weighted VLAD^[24]	2.86	13.05
MCNN^[7]	2.24	8.50
Bi-ConvLSTM^[22]	2.10	7.60
RDCAF	1.79	2.32

方法	MAE	RMSE
MCNN^[7]	277.0	426.0
C-MTL^[8]	252.0	514.0
SwitchCNN^[9]	228.0	445.0
CL-CNN^[23]	132.0	191.0
RDCAF	108.5	180.6

方法	MAE	RMSE
无注意力机制结构	9.20	15.40
无残差密集连接结构	11.40	16.90
RDCAF	8.51	14.20

Crowd Counting Algorithm Based on Residual Dense Connection and Attention Fusion

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