Adaptive Feature Fusion Object Tracking Based on Circulant Structure with Kernel

Gaopeng Zhao; Yupeng Shen; Jianyu Wang

doi:10.3788/AOS201737.0815001

Acta Optica Sinica, Volume. 37, Issue 8, 0815001(2017)

Adaptive Feature Fusion Object Tracking Based on Circulant Structure with Kernel

Gaopeng Zhao^*, Yupeng Shen, and Jianyu Wang

Author Affiliations

Department of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China

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Figures & Tables(10)

Fig. 1. Tracking results of CSK algorithm. (a) Illumination variation; (b) occlusion; (c) scale variation

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Fig. 2. Tracking results of the proposed algorithm and CSK algorithm. (a) Fish; (b) car24; (c) suv

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Fig. 3. Center location error comparison of the proposed algorithm and CSK algorithm. (a) Fish; (b) car24; (c) suv

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Fig. 4. Tracking results of different tracking algorithms. (a) Bird2; (b) bolt2; (c) human8; (d) jogging2; (e) car1; (f) walking2

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Fig. 5. Distance precision of tracking algorithms. (a) Bird2; (b) bolt2; (c) human8; (d) jogging2; (e) car1; (f) walking2

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Fig. 6. Success rate of tracking algorithms. (a) Bird2; (b) bolt2; (c) human8; (d) jogging2; (e) car1; (f) walking2

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Table 1. Characteristics of video sequences in the experiment

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Table 1. Characteristics of video sequences in the experiment

Sequence	Characteristic	Sequence	Characteristic
Car24	Illumination variation	Human8	Illumination variation, scale variation
Fish	Illumination variation	Jogging2	Occlusion
Suv	Occlusion	Car1	Illumination variation, scale variation
Bird2	Occlusion, deformation	Walking2	Scale variation, occlusion
Bolt2	Deformation, background clutters

Table 2. Distance precision of different tracking algorithms

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Table 2. Distance precision of different tracking algorithms

Sequence	Algorithm
Sequence	KCF	STRUCK	TLD	MIL	CT	DSST	Proposed algorithm
Bird2	0.474	0.545	$\begin{matrix} \bar{0.869} \end{matrix}$	0.646	0.101	0.474	0.979
Bolt2	0.017	0.109	0.013	1.000	0.638	0.020	$\begin{matrix} \bar{0.993} \end{matrix}$
Human8	1.000	$\begin{matrix} \bar{0.195} \end{matrix}$	0.187	0.156	0.078	1.000	1.000
Jogging2	0.162	0.254	$\begin{matrix} \bar{0.856} \end{matrix}$	0.182	0.166	0.185	1.000
Car1	$\begin{matrix} \bar{0.738} \end{matrix}$	0.330	0.591	0.239	0.141	1.000	1.000
Walking2	0.438	$\begin{matrix} \bar{0.966} \end{matrix}$	0.424	0.406	0.432	1.000	1.000

Table 3. Success rate of different tracking algorithms

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Table 3. Success rate of different tracking algorithms

Sequence	Algorithm
Sequence	KCF	STRUCK	TLD	MIL	CT	DSST	Proposed algorithm
Bird2	0.474	0.525	$\begin{matrix} \bar{0.707} \end{matrix}$	0.595	0.101	0.474	0.979
Bolt2	0.006	0.006	0.006	0.965	0.419	0.010	$\begin{matrix} \bar{0.686} \end{matrix}$
Human8	$\begin{matrix} \bar{0.304} \end{matrix}$	0.132	0.132	0.156	0.007	1.000	1.000
Jogging2	0.159	0.162	0.156	0.162	0.140	$\begin{matrix} \bar{0.182} \end{matrix}$	0.996
Car1	0.053	0.053	0.293	0.053	0.007	$\begin{matrix} \bar{0.604} \end{matrix}$	1.000
Walking2	0.378	$\begin{matrix} \bar{0.434} \end{matrix}$	0.338	0.380	0.384	1.000	1.000

Table 4. Complexity analysisframe·s-1
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Table 4. Complexity analysisframe·s-1
Algorithm Sequence
Bird2 Bolt2 Human8 Jogging Car1 Walking2
CSK 51.4 130.5 139.1 81.1 175.1 80.7
DSST 7.2 21.6 17.5 10.1 18.0 12.5
Proposed algorithm 20.0 39.0 34.0 28.7 49.6 30.9

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Gaopeng Zhao, Yupeng Shen, Jianyu Wang. Adaptive Feature Fusion Object Tracking Based on Circulant Structure with Kernel[J]. Acta Optica Sinica, 2017, 37(8): 0815001

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