适用于无监督行人重识别的反向骨干网

doi:10.13229/j.cnki.jdxbgxb.20240916

Abstract

Abstract:

The purpose of person re-identification （re-id） is to identify images of the same person on different cameras. Although unsupervised models have better generalization than supervised models， unsupervised clustering will be more susceptible to noise interference. To address this problem， this paper proposes a model reverse backbone net （RBNet） that can reduce noise interference， using RBNet to learn the keypoints of the human body output from the pose detection model， adjusting the local spatial information and generating masks， and augmenting the specified location attention with the generated masks. The experimental results show that comparing baseline's cross-domain experimental results from Market-1501 to DukeMTMC-reID， mAP is enhanced by 7.0% and Rank-1 by 6.4%. Strengthening the attention to different local information can effectively improve the model accuracy.

Key words: artificial intelligence, person re-identification, human keypoints, unsupervised domain adaptation

CLC Number:

TP391.4

Peng YU,Yan PIAO. Reverse backbone net for unsupervised person re-identification[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(11): 3309-3317.

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Method	Market-1501		DukeMTMC-reID		MSMT17
	mAP	R-1	mAP	R-1	mAP	R-1
GCP ^［21］	88.9	95.2	78.6	89.7	—	—
MMGA ^［5］	87.2	95.0	78.1	89.5	—	—
RGA-SC ^［8］	88.4	96.1	—	—	57.5	80.3
AANet-50 ^［1］	82.45	93.89	72.56	86.42	—	—
PCB ^［3］	81.6	93.8	69.2	83.3	—	—
OSNet ^［9］	84.9	94.8	73.5	88.6	52.9	78.7
FastReID ^［22］	88.2	95.4	79.8	89.6	59.9	83.3
CBDB-Net ^［23］	85.0	94.4	74.3	87.7	—	—
PAT ^［24］	88.0	95.4	78.2	88.8	—	—
CDNet ^［25］	86.0	95.1	76.8	88.6	54.7	78.9
RBNet（conv 1）	88.5	95.6	80.0	89.7	60.8	84.9
RBNet（relayer 1）	89.1	96.1	81.1	90.0	62.7	85.1
RBNet（relayer 2）	84.3	94.0	73.9	83.3	49.4	77.1

M-> D
基于模型	model	mAP	Rank-1	Rank-5	Rank-10
ResNet-50	MMCL ^［27］	51.4	72.4	—	—
E_app （ResNet 50）	DG-Net++ ^［28］	63.8	78.9	—	—
ResNet-50	GDS ^［29］	55.1	73.1	—	—
ResNet 50	GPP ^［30］	54.4	74.0	—	—
ResNet 50	MMT-500 ^［14］	63.1	76.8	88.0	92.2
MMT （ResNet 50）	MetaCam ^［12］	65.0	79.5
fast-reid	GLT ^［26］	69.2	82.0	90.2	92.8
MMT （ResNet 50）	MMT+RBNet	70.1	83.2	90.8	92.9
D-> M
基于模型	model	mAP	Rank-1	Rank-5	Rank-10
ResNet-50	MMCL	60.4	84.4	—	—
E_app（ResNet-50）	DG-Net++	61.7	82.1	—	—
ResNet-50	GDS	61.2	81.1	—	—
ResNet 50	GPP+	63.8	84.1	—	—
ResNet 50	MMT-500	71.2	87.7	94.9	96.9
MMT （ResNet 50）	MetaCam	76.5	90.1	—	—
fast-reid	GLT	79.5	92.2	96.5	97.8
MMT （ResNet 50）	MMT+RBNet	79.8	92.4	96.7	97.9

M->D
Method	mAP	Rank-1	Rank-5	Rank-10
APNet-c3 ^［31］	22.8	37.7	52.4	59.0
OSNet ^［9］	26.7	48.5	62.3	67.4
resnet 50	17.3	32.7	47.1	53.4
resnet 50+RBNet	27.4	49.4	62.9	67.7
D->M
Method	mAP	Rank-1	Rank-5	Rank-10
APNet-c3	23.7	50.9	66.6	72.6
OSNet	26.1	57.7	73.7	80.0
resnet 50	17.7	43.9	50.9	67.5
resnet 50+RBNet	27.5	60.6	75.7	80.8

Reverse backbone net for unsupervised person re-identification

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