基于多池化融合与背景消除网络的跨数据集行人再识别方法

doi:10.11959/j.issn.1000-436x.2020181

Abstract

Abstract:

The existing cross-dataset person re-identification methods were generally aimed at reducing the difference of data distribution between two datasets,which ignored the influence of background information on recognition performance.In order to solve this problem,a cross-dataset person re-ID method based on multi-pool fusion and background elimination network was proposed.To describe both global and local features and implement multiple fine-grained representations,a multi-pool fusion network was constructed.To supervise the network to extract useful foreground features,a feature-level supervised background elimination network was constructed.The final network loss function was defined as a multi-task loss,which combined both person classification loss and feature activation loss.Three person re-ID benchmarks were employed to evaluate the proposed method.Using MSMT17 as the training set,the cross-dataset mAP for Market-1501 was 35.53%,which was 9.24% higher than ResNet50.Using MSMT17 as the training set,the cross-dataset mAP for DukeMTMC-reID was 41.45%,which was 10.72% higher than ResNet50.Compared with existing methods,the proposed method shows better cross-dataset person re-ID performance.

Key words: person re-identification, cross-dataset, background elimination, multi-pool fusion, deep learning

CLC Number:

TP391

Yanfeng LI,Bin ZHANG,Jia SUN,Houjin CHEN,Jinlei ZHU. Cross-dataset person re-identification method based on multi-pool fusion and background elimination network[J]. Journal on Communications, 2020, 41(10): 70-79.

Figures/Tables 15

References 27

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数据集	网络	Rank-1	Rank-5	Rank-10	mAP
	ResNet50	82.40%	93.15%	95.50%	60.76%
Market-1501	G-L	87.97%	95.63%	97.18%	68.18%
	MPF	91.90%	97.01%	98.25%	76.35%
	ResNet50	74.66%	85.92%	89.79%	54.23%
DukeMTMC-reID	G-L	78.90%	88.78%	91.52%	61.10%
	MPF	83.94%	92.11%	94.21%	68.29%
	ResNet50	60.85%	74.85%	80.68%	30.67%
MSMT17	G-L	66.00%	78.97%	83.38%	34.43%
	MPF	72.07%	83.07%	86.50%	40.70%

训练集	网络	Rank-1	Rank-5	Rank-10	mAP
	ResNet50	47.60%	65.03%	71.80%	20.45%
DukeMTMCreID	MPF	58.11%	73.97%	79.25%	28.04%
	MPF+背景消除	62.48%	78.12%	84.27%	30.72%
	ResNet50	54.93%	71.20%	77.47%	26.29%
MSMT17	MPF	62.33%	78.51%	84.09%	32.22%
	MPF+背景消除	67.85%	82.52%	86.97%	35.53%

训练集	网络	Rank-1	Rank-5	Rank-10	mAP
	ResNet50	32.50%	47.40%	52.52%	16.43%
Market-1501	MPF	43.05%	57.23%	63.51%	24.64%
Market-1501	MPF+背景消除	55.57%	68.81%	74.06%	30.73%
MSMT17	ResNet50	56.06%	70.29%	75.58%	34.20%
MSMT17	MPF	60.09%	74.01	78.54%	36.97%
	MPF+背景消除	64.91%	77.88	82.10%	41.45%

训练集	网络	Rank-1	Rank-5	Rank-10	mAP
	ResNet50	14.62%	23.82%	28.75%	4.31%
Market-1501	MPF	23.92%	34.09%	39.91%	7.87%
Market-1501	MPF+背景消除	32.80%	44.91%	50.45%	10.76%
DukeMTMCreID	ResNet50	22.43%	34.30%	39.88%	6.95%
DukeMTMCreID	MPF	29.35%	41.59%	47.48%	9.36%
	MPF+背景消除	38.65%	51.20%	56.67%	13.19%

数据集	ResNet50/min	MPF/min	MPF+背景消除/min
Market-1501	66	96	200
DukeMTMC-reID	100	150	325
MSMT17	200	240	560

Cross-dataset person re-identification method based on multi-pool fusion and background elimination network

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网络	Rank-1	Rank-5	Rank-10	mAP
TJ-AIDL^[15]	44.3%	59.6%	65.0%	23.0%
MMFA^[16]	45.3%	59.8%	66.3%	24.7%
HHL^[17]	46.9%	61.0%	66.7%	27.2%
PT-GAN^[18]	27.4%	—	50.7%	—
SP-GAN^[19]	41.1%	56.6%	63.0%	22.3%
ATNet^[20]	45.1%	59.5%	64.2%	24.9%
MPF+背景消除	55.57%	68.81%	74.06%	30.73%

网络	Rank-1	Rank-5	Rank-10	mAP
TJ-AIDL^[15]	58.2%	74.8%	81.1%	26.5%
MMFA^[16]	56.7%	75.0%	81.8%	27.4%
HHL^[17]	62.2%	78.8%	84.0%	31.4%
PT-GAN^[18]	38.6%	—	66.1%	—
SP-GAN^[19]	51.5%	70.1%	76.8%	22.8%
ATNet^[20]	55.7%	73.2%	79.4%	25.6%
MPF+背景消除	62.48%	78.12%	84.27%	30.72%