Cross-domain Crowd Counting Model Based on Frequency Domain Enhancement

doi:10.16182/j.issn1004731x.joss.25-0896

Abstract

Abstract:

Crowd counting takes video surveillance data as input and can be applied to the construction of city digital twin platforms, virtual city modeling and smart city management, etc. However, when there are data domain differences between the application scenario and training scenario, counting performance often significantly decreases. A cross-domain crowd counting model based on frequency domain enhancement is proposed. To alleviate the distribution differences between domains, a frequency domain feature enhancement module and a domain invariant frequency domain adapter module are constructed: the former uses discrete cosine transform to extract key statistical features to enhance spatial representation ability, while the latter decomposes amplitude and phase spectra based on fast Fourier transform and suppresses domain specific amplitude information through attention mechanism to extract domain invariant features. To cope with complex background interference, a parallel dual attention module is designed to focus on the foreground region. To address the challenges posed by drastic scale changes, a multi-scale feature aggregation module is proposed to achievethe fusion of decoder features at different scales, thereby enhancing the model robustness. Cross-domain simulation experiments are conducted on four public crowd counting datasets, and the results showed that the proposed method achieved the lowest counting error in most challenging scenarios, outperforming current mainstream methods and providing effective supports for robust and high-precision crowd dynamic simulation.

Key words: crowd counting, frequency-domain modeling, domain generalization, simulation input modeling, smart city

CLC Number:

TP391.4

Zhang De, Liang Zishan, Liu Ningning. Cross-domain Crowd Counting Model Based on Frequency Domain Enhancement[J]. Journal of System Simulation, 2026, 38(1): 158-173.

Figures/Tables 15

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Table 1

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Table 2

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Table 3

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Table 5

Table 6

Effect of hyper-parameter β in DIFA

$β$	SHA→SHB		SHA→UQ
$β$	MAE	MSE	MAE	MSE
0.1	18.4	31.6	214.4	495.1
0.3	19.0	28.1	159.6	276.2
0.5	12.1	21.5	124.2	222.3
0.7	19.1	27.4	158.6	272.8
0.9	23.3	34.6	224.5	629.7

Table 6

References 33

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方法	发表来源	SHA→SHB		SHA→UQ		SHA→UC
方法	发表来源	MAE	MSE	MAE	MSE	MAE	MSE
BL^[22]	ICCV (2019)	15.9	25.8	166.7	287.6	432.7	683.3
SPN+L2SM^[23]	ICCV (2019)	21.2	38.7	227.2	405.2	332.4	588.4
DMNet^[24]	NeurIPS (2020)	22.6	33.9	148.9	281.3	417.7	664.2
LibraNet^[25]	ECCV (2020)	11.9	20.7	127.9	204.9	—	—
RDBT^[12]	ACM MM (2020)	13.4	29.3	175.0	294.8	368.0	518.9
LSC^[26]	TPAMI (2020)	23.4	35.9	207.6	371.9	456.8	696.1
AutoScale^[27]	IJCV (2022)	23.1	39.0	—	—	—	—
FIDTM^[28]	TMM (2022)	17.4	35.1	193.6	457.9	423.1	602.5
GENet^[10]	TCSVT (2022)	14.1	24.6	184.8	371.1	333.5	504.0
CSS-CCNN++^[29]	ECCV (2022)	—	—	472.4	—	468.1	—
CTASNet^[30]	TCSVT (2023)	13.4	25.2	133.4	224.0	—	—
DCCUS^[11]	AAAI (2023)	12.6	24.6	119.4	216.6	241.8	402.2
MFFNet^[7]	TIM (2023)	12.7	30.2	142.1	271.1	323.5	482.8
CrowdFormer^[31]	JVCI (2023)	16.0	26.0	147.6	300.8	—	—
MPCount^[21]	CVPR (2024)	11.4	19.7	115.7	199.8	172.6	257.1
MGFNet^[8]	Neurocomputing (2024)	11.3	19.9	117.1	202.5	319.8	475.3
CCSC^[32]	TMM (2025)	11.7	20.8	124.5	220.9	—	—
FDE-Net	本文	12.1	21.5	124.2	222.3	184.7	370.0

方法	发表来源	SHB→SHA		SHB→UQ		SHB→UC
方法	发表来源	MAE	MSE	MAE	MSE	MAE	MSE
BL^[22]	ICCV (2019)	138.1	228.1	226.4	411.0	645.5	940.1
SPN+L2SM^[23]	ICCV (2019)	126.8	203.9	—	—	—	—
DMNet^[24]	NeurIPS (2020)	142.4	241.3	223.6	418.6	1093.9	1405.6
RDBT^[12]	ACM MM (2020)	112.2	218.2	211.3	381.9	—	—
LSC^[26]	TPAMI (2020)	153.5	250.4	279.0	490.0	731.2	1065.9
FIDTM^[28]	TMM (2022)	147.6	282.2	—	—	—	—
GENet^[10]	TCSVT (2022)	144.4	233.1	318.9	556.9	751.8	1106.1
CTASNet^[30]	TCSVT (2023)	126.9	222.3	183.8	319.2	—	—
DCCUS^[11]	AAAI (2023)	121.8	203.1	179.1	316.2	548.2	895.3
MFFNet^[7]	TIM (2023)	107.3	188.5	—	—	—	—
CrowdFormer^[31]	JVCI (2023)	121.6	208.8	304.4	586.1	—	—
MPCount^[21]	CVPR (2024)	99.6	182.9	165.6	290.4	492.8	860.9
MGFNet^[8]	Neurocomputing (2024)	105.7	183.7	176.8	307.2	631.4	927.1
CCSC^[32]	TMM (2025)	100.9	174.9	172.8	312.6	—	—
FDE-Net	本文方法	110.7	191.9	187.1	326.7	478.2	783.2

方法	发表来源	UQ→SHA		UQ→SHB		UQ→UC
方法	发表来源	MAE	MSE	MAE	MSE	MAE	MSE
BL^[22]	ICCV19	67.8	118.2	16.2	27.5	387.4	614.5
SPN+L2SM^[23]	ICCV19	73.4	119.4	—	—	—	—
DMNet^[24]	NeurIPS20	70.1	130.3	14.0	27.1	391.1	583.8
LibraNet^[25]	ECCV20	67.0	109.2	11.6	22.0	—	—
LSC^[26]	TPAMI20	97.1	154.7	12.5	21.9	535.2	822.3
GENet^[10]	TCSVT22	76.8	124.3	10.6	15.3	336.3	499.2
CTASNet^[30]	TCSVT23	68.7	118.2	12.0	23.3	278.7	445.0
DCCUS^[11]	AAAI23	67.4	112.8	12.1	20.9	143.4	224.1
CrowdFormer^[31]	JVCI23	70.8	114.9	10.3	16.0	—	—
MPCount^[21]	CVPR2024	65.5	110.1	12.3	24.1	196.7	341.2
MGFNet^[8]	Neurocomputing24	60.4	98.6	9.7	14.8	259.3	379.7
CCSC^[32]	TMM25	67.0	111.5	11.3	19.7	—	—
FDE-Net	本文方法	63.5	104.0	12.8	23.0	126.3	176.1

模块				SHA→SHB		SHA→UQ
FDFE	DIFA	MFA	PDA	MAE	MSE	MAE	MSE
				33.1	56.1	335.5	520.2
√				17.6	29.6	167.4	267.9
√	√			16.1	24.6	164.0	266.8
√	√	√		14.2	23.6	138.7	240.7
√	√	√	√	12.1	21.5	124.2	222.3

插入位置	SHA→SHB		SHA→UQ
插入位置	MAE	MSE	MAE	MSE
基线模型	33.1	56.1	335.5	520.2
Encoder1后面	25.8	35.6	165.1	268.3
Encoder2后面	18.8	29.0	157.4	286.1
Encoder3后面	14.1	26.5	138.1	241.0
Encoder4后面	14.0	29.0	147.8	262.3
Encoder5后面	17.3	25.6	208.9	325.9
Encoder3后面和Encoder4后面	12.1	21.5	124.2	222.3