Person Re-Identification Based on View Information Embedding

Xiaojun Bi and Hao Wang^*

Author Affiliations

College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China

show less

DOI: 10.3788/AOS201939.0615007 Cite this Article Set citation alerts

Xiaojun Bi, Hao Wang. Person Re-Identification Based on View Information Embedding[J]. Acta Optica Sinica, 2019, 39(6): 0615007 Copy Citation Text

show less

Fig. 1. Structure of PSE network model

Fig. 2. Model based perspective information embedding

Fig. 3. Depthwise separable convolution

Fig. 4. Improved depthwise separable convolution

Fig. 5. Structure of depthwise separable module

	Method	Market1501		Duke-MTMC-reID			MARS
	Method	rank-1 /%	mAP /%	rank-1 /%		mAP /%	rank-1 /%	mAP /%
Proposed	Except perspective	83.6			62.6				74.1	53.7	67.7	50.1
	All	89.9			71.6				79.9	61.7	74.1	57.6

Table 1. Results of perspective predictor module verification experiment

	Method	Market1501		Duke-MTMC-reID			MARS
	Method	rank-1 /%	mAP /%	rank-1 /%		mAP /%	rank-1 /%	mAP /%
Proposed	Except SE-Block	87.0			67.5				77.8	59.6	71.2	54.2
	All	89.9			71.6				79.9	61.7	74.1	57.6

Table 2. Results of improved depthwise separable convolution verification experiment

	Method	Market1501		Duke-MTMC-reID			MARS
	Method	rank-1 /%	mAP /%	rank-1 /%		mAP /%	rank-1 /%	mAP /%
Proposed	Except Mid-level-feature	87.4			70.9				79.5	57.8	72.1	55.9
	All	89.9			71.6				79.9	61.7	74.1	57.6

Table 3. Verification experiment results of mid-level feature method

	S/N	Improved method			Market1501			Duke-MTMC-reID			MARS
	S/N	IM1	IM2	IM3	rank-1 /%		mAP /%	rank-1 /%	mAP /%		rank-1 /%	mAP /%
PSE	①	-				-				-			87.7	69.0	79.8	62.0	72.1	56.9
	②	-				-				Y			86.8	66.5	77.5	60.5	70.5	54.9
	③	-				Y				-			82.5	64.2	70.1	54.3	68.4	49.3
	④	-				Y				Y			85.3	65.8	72.5	57.2	70.2	53.8
Ours	⑤	Y				-				-			87.5	69.0	79.4	61.1	70.9	57.1
	⑥	Y				-				Y			85.9	66.5	75.6	59.9	67.8	54.6
	⑦	Y				Y				-			86.6	65.6	77.4	59.4	70.1	55.1
	⑧	Y				Y				Y			89.9	71.6	79.9	61.7	74.1	57.6

Table 4. Results of improved model verification experiment

Table 5. Results of algorithm running speed comparison experiment

Method	Market1501		Duke-MTMC-reID		MARS
Method	rank-1 /%	mAP /%	rank-1 /%	mAP /%	rank-1 /%	mAP /%
P2S(point to set)	70.7			44.3			-	-	-	-
Spindle	76.9			-			-	-	-	-
Consistent aware	80.9			55.6			-	-	-	-
GAN(generative adversarial networks)	78.1			56.2			67.7	47.1	-	-
Latent parts	80.3			57.5			-	-	71.8	56.1
ResNet+OIM(online instance matching)	82.1			-			68.1	-	-	-
ACRN(attribute-complementary re-ID net)	83.6			62.6			72.6	52.0	-	-
SVD(singular value decomposition)	82.3			62.1			76.7	56.8	-	-
Part aligned	81.0			63.4			-	-	-	-
PDC(pose-driven deep convolutional model)	84.1			63.4			-	-	-	-
JLML(jointly learning multi-loss)	85.1			65.5			-	-	-	-
DPFL	88.6			72.6			79.2	60.6	-	-
Forest	-			-			-	-	70.6	50.7
DGM(dynamic graph matching)+IDE	-			-			-	-	65.2	46.8
QMA	-			-			-	-	73.7	51.7
ResNet baseline	82.6			59.8			71.5	50.3	64.5	49.5
PSE	87.7			69.0			79.8	62.0	72.1	56.9
Proposed algorithm	89.9			71.6			79.9	61.7	74.1	57.6

Table 6. Comparison of algorithm results

Xiaojun Bi, Hao Wang. Person Re-Identification Based on View Information Embedding[J]. Acta Optica Sinica, 2019, 39(6): 0615007

Download Citation