Tracking Algorithm Based on Video Person Reidentification and Spatiotemporal Feature Fusion

Guancheng Hui; Kaifang Li; Ming Xin; Miaohui Zhang

doi:10.3788/LOP202259.1215004

Laser & Optoelectronics Progress, Volume. 59, Issue 12, 1215004(2022)

Tracking Algorithm Based on Video Person Reidentification and Spatiotemporal Feature Fusion

Guancheng Hui¹, Kaifang Li¹, Ming Xin³, and Miaohui Zhang^1,2、*

Author Affiliations

¹School of Artificial Intelligence, Henan University, Kaifeng 475004, Henan , China

²Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, Henan , China

³School of Computer Science and Engineering, Beihang University, Beijing 100191, China

show less

Abstract Get PDF(in Chinese)

Figures & Tables(13)

Fig. 1. Joint network architecture

Download full size

Fig. 2. Encoder-decoder network

Download full size

Fig. 3. Detection branch outputing the heat map, center offset, and box size to determine the information of the bounding box and the re-identify branch outputing the classification probability of each ID

Download full size

Fig. 4. Tracking result comparison between the MSC network and the original ResNet-34 network. (a) Detection result of the original ResNet-34 network; (b) detection result of the MSC network; (c) structure diagram of the original ResNet-34 network; (d) structure diagram of the MSC network

Download full size

Fig. 5. Candidate box selection based on unified scoring mechanism

Download full size

Fig. 6. Output results of the proposed method on MOT17 test set

Download full size

Fig. 7. Reasoning time of MSC network and ResNet-34 network on three data sets

Download full size

Table 1. Experimental platform parameters

View table

Table 1. Experimental platform parameters

Configuration	Parameter
Operating system	Ubuntu 16.04
RAM（random processing unit）	128 G
CPU（central processing unit）	2.50 GHz E5-2678 v3
GPU（graphics processing unit）	Tesla T4 16 G
Software platform	Pytorch 1.1 Python 3.6

Table 2. Recognition feature dimensions evaluated on the MOT17 validation set
View table
Table 2. Recognition feature dimensions evaluated on the MOT17 validation set
Dimension MOTA IDF1 IDs Time /s
512 68.5 73.7 312 24.1
256 68.5 72.8 337 26.1
128 69.1 72.5 299 26.6
64 69.2 72.3 283 26.8

Table 3. Evaluation of the three elements associated with the evaluation data on the MOT17 validation set
View table
Table 3. Evaluation of the three elements associated with the evaluation data on the MOT17 validation set
Box IoU re-ID Features Kalman Filter MOTA IDF1 IDs
√ 67.8 67.2 648
√ 68.1 70.3 435
√ √ 68.9 71.8 342
√ √ √ 69.1 72.8 299

Table 4. Comparison between MSC network and ResNet-34 network on MOT17 validation set
View table
Table 4. Comparison between MSC network and ResNet-34 network on MOT17 validation set
Network MOTA IDF1 IDs
ResNet-34 63.6 67.2 435
MSC 69.1 72.8 299

Table 5. Result using different datasets for training
View table
Table 5. Result using different datasets for training
Dataset Number of images Number of boxes Number of identities MOTA IDF1 IDs
MOT17 5×10³ 112×10³ 0.5×10³ 69.1 72.8 299
MIX 54×10³ 270×10³ 8.7×10³ 73.7 80.1 209

Table 6. Comparison of results of different methods

View table

Table 6. Comparison of results of different methods

Dataset	Tracker	MOTA	IDF1	MT /%	ML /%	IDs	Time /s
MOT16	EAMTT^［23］	52.5	53.3	19.9	34.9	910	<5.5
	SORTwHPD16^［24］	59.8	53.8	25.4	22.7	1423	<8.6
	DeepSORT_2^［25］	61.4	62.2	32.8	18.2	781	<6.4
	RAR16wVGG^［26］	63.0	63.8	39.9	22.1	482	<1.4
	VMaxx^［27］	62.6	49.2	32.7	21.1	1389	<3.9
	TubeTK^［28］	64.0	59.4	33.5	19.4	1117	1.0
	JDE^［3］	64.4	55.8	35.4	20.0	1544	18.5
	TAP^［29］	64.8	73.5	38.5	21.6	571	<8.0
	CNNMTT^［30］	65.2	62.2	32.4	21.3	946	<5.3
	POI^［31］	66.1	65.1	34.0	20.8	805	<5.0
	CTackerVI^［32］	67.6	57.2	32.9	23.1	1897	6.8
	Proposed method	74.7	80.2	38.10	21.47	210	13.3
MOT17	SST^［33］	52.4	49.5	21.4	30.7	8431	<3.96
	TubeTK^［28］	63.0	58.6	31.2	19.9	4137	3.0
	CTackerVI^［32］	66.6	57.4	32.2	24.2	5529	6.8
	CenterTack^［34］	67.3	59.9	34.6	24.6	2898	22.0
	Proposed method	73.7	80.1	36.99	22.89	209	12.70
MOT20	ArTIST-T^［35］	53.6	51.0	31.6	28.1	1531
	MPNTrack^［36］	57.6	59.1	38.2	22.5	1210
	Proposed method	66.4	72.8	46.87	14.84	1403	12.0

Tools

Get Citation

Copy Citation Text

Guancheng Hui, Kaifang Li, Ming Xin, Miaohui Zhang. Tracking Algorithm Based on Video Person Reidentification and Spatiotemporal Feature Fusion[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215004

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites