Correlation Filter Object Tracking Based on Adaptive Spatiotemporal Regularization

Fig. 1. Process of obtaining initial frame saliency awareness reference weight for Soccer video sequence. (a) First frame image; (b) saliency detection result; (c) result of saliency detection after treatment; (d) saliency image; (e) saliency awareness reference weight for initial frame

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Fig. 2. Visualization of four different spatial regularization weights. (a) W₁; (b) W₂; (c) W₃; (d) W₄

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Fig. 3. Response of object in different cases for Box video sequence. (a) Object for 130^th frame; (b) response for 130^th frame; (c) object for 460^th frame; (d) response for 460^th frame

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Fig. 4. Distance precision and success rate on OTB-2015 dataset. (a) Distance precision curve; (b) success rate curve

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Fig. 5. Success rate of 4 different attribute video sequences on OTB-2015 dataset. (a) Deformation; (b) out-of-plane rotation; (c) occlusion; (d) out of view

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Fig. 6. Tracking results of our algorithm and comparison algorithms on 4 video sequences. (a) Box; (b) dragonbaby; (c) shaking; (d) soccer

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Table 1. Relevant parameters setting of ADMM in the process of algorithm optimization
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Table 1. Relevant parameters setting of ADMM in the process of algorithm optimization
ADMM Relevant parameter
Solving filtering function and temporal regularization parameter γ⁽⁰⁾=1,γ^max=10,α=0.1,N_I=2
Solving spatial regularization weight ζ⁽⁰⁾=1,ζ^max=100,β=10,N'_I=2

Table 2. Comparison of tracking performance of algorithm in different parameter settingsunit: %
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Table 2. Comparison of tracking performance of algorithm in different parameter settingsunit: %
Parameter setting DP SR
ζ⁽⁰⁾=1,ζ^max=1000,β=10,N'_I=2 86.2 65.1
ζ⁽⁰⁾=1,ζ^max=10000,β=10,N'_I=2 84.1 63.8
ζ⁽⁰⁾=1,ζ^max=100,β=10,N'_I=2 86.4 65.6
ζ⁽⁰⁾=1,ζ^max=100,β=10,N'_I=3 84.9 64.3

Table 3. Comparison of average tracking speeds on OTB-2015 dataset
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Table 3. Comparison of average tracking speeds on OTB-2015 dataset
Algorithm DSST BACF SRDCF STRCF CNN-SVM Ours
Average tracking speed /(frame·s^-1) 31.8 22.1 3.3 13.7 N 12.1

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Xiangming Qi, Wei Chen. Correlation Filter Object Tracking Based on Adaptive Spatiotemporal Regularization[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0215007

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Paper Information

Category: Machine Vision

Received: Jun. 16, 2020

Accepted: Jul. 24, 2020

Published Online: Jan. 11, 2021

The Author Email: Chen Wei (1163186035@qq.com)

DOI:10.3788/LOP202158.0215007

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Relevant parameters setting of ADMM in the process of algorithm optimization

Table 1. Relevant parameters setting of ADMM in the process of algorithm optimization

Table 2. Comparison of tracking performance of algorithm in different parameter settingsunit: %

Table 2. Comparison of tracking performance of algorithm in different parameter settingsunit: %

Table 3. Comparison of average tracking speeds on OTB-2015 dataset

Table 3. Comparison of average tracking speeds on OTB-2015 dataset