Multi-Scale Kernel Correlation Filter Algorithm for Visual Tracking Based on the Fusion of Adaptive Features

Faling Chen; Qinghai Ding; Zheng Chang; Hongyu Chen; Haibo Luo; Bin Hui; Yunpeng Liu

doi:10.3788/AOS202040.0315001

Journals >Acta Optica Sinica >Volume 40 >Issue 3 >Page 0315001 > Article

Acta Optica Sinica
Vol. 40, Issue 3, 0315001 (2020)

Multi-Scale Kernel Correlation Filter Algorithm for Visual Tracking Based on the Fusion of Adaptive Features

Faling Chen^{1、2、3、4、5、*}, Qinghai Ding^1、6, Zheng Chang^{1、2、4、5}, Hongyu Chen^{1、2、3、4、5}, Haibo Luo^{1、2、4、5}, Bin Hui^{1、2、4、5}, and Yunpeng Liu^{1、2、4、5}

Author Affiliations

¹Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

²Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110169, China

³University of Chinese Academy of Sciences, Beijing 100049, China

⁴Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China

⁵Key Laboratory of Image Understanding and Computer Vision, Shenyang, Liaoning 110016, China

⁶Space Star Technology Co., Ltd., Beijing 100086, China

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DOI: 10.3788/AOS202040.0315001 Cite this Article Set citation alerts

Faling Chen, Qinghai Ding, Zheng Chang, Hongyu Chen, Haibo Luo, Bin Hui, Yunpeng Liu. Multi-Scale Kernel Correlation Filter Algorithm for Visual Tracking Based on the Fusion of Adaptive Features[J]. Acta Optica Sinica, 2020, 40(3): 0315001 Copy Citation Text

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Fig. 1. Schematic of adaptive features fusion process

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Fig. 2. Relationship between weight adjustment ρ and the target tracking performance

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Fig. 3. Distance precision curves and overlap precision curves of three target tracking algorithms. (a) Distance precision; (b) overlap precision

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Fig. 4. Comparisons of estimated scale by the proposed algorithm and actual scale on four sequences.(a) Blurcar2; (b) Dog1; (c) Doll; (d) Carscale

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Fig. 5. Distance precision curves and overlap precision curves of different target tracking algorithms. (a) Distance precision; (b) overlap precision

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Fig. 6. Comparison of tracking results among five algorithms on David sequence

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Fig. 7. Comparison of tracking results among five algorithms on Basketball sequence

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Fig. 8. Comparison of tracking results among five algorithms on Carscale sequence

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Fig. 9. Comparison of tracking results among five algorithms on Jogging1 sequence

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Fig. 10. Comparison of tracking results among five algorithms on Trellis sequence

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Fig. 11. Comparison of tracking results among five algorithms on Trellis sequence

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Sequence	Mean E_CL	P_d/% (E_CL=20)	P_o /% (S_o=0.5)
Blurcar2	3.42	100.0	100.0
Dog1	3.81	100.0	100.0
Doll	2.26	99.3	99.6
Carscale	3.84	100.0	100.0

Table 1. Tracking results of the proposed algorithm on four scale variation sequences

Algorithm	IV	DEF	SV	OCC	MB	FM	IPR	OPR	OV	BC	LR
Proposed	0.780	0.737	0.739	0.761	0.653	0.581	0.704	0.751	0.665	0.714	0.424
DSST	0.730	0.636	0.738	0.692	0.544	0.513	0.768	0.725	0.511	0.694	0.497
KCF	0.657	0.698	0.648	0.695	0.571	0.534	0.691	0.678	0.590	0.676	0.387
Struck	0.558	0.521	0.639	0.564	0.551	0.604	0.617	0.597	0.539	0.585	0.545
SCM	0.594	0.586	0.672	0.640	0.339	0.333	0.597	0.618	0.429	0.578	0.305
CN	0.576	0.607	0.599	0.621	0.551	0.482	0.674	0.645	0.438	0.629	0.408
TLD	0.537	0.512	0.606	0.563	0.518	0.551	0.584	0.596	0.576	0.428	0.349
VTD	0.557	0.501	0.597	0.545	0.375	0.352	0.599	0.620	0.462	0.571	0.168
VTS	0.573	0.487	0.582	0.534	0.375	0.353	0.579	0.604	0.455	0.578	0.187
CXT	0.501	0.422	0.550	0.491	0.509	0.515	0.610	0.574	0.510	0.443	0.371

Table 2. P_d scores of the top ten algorithms on eleven attributes

Algorithm	IV	DEF	SV	OCC	MB	FM	IPR	OPR	OV	BC	LR
Proposed	0.712	0.733	0.721	0.738	0.591	0.532	0.674	0.702	0.672	0.648	0.419
DSST	0.681	0.610	0.640	0.632	0.528	0.503	0.679	0.632	0.512	0.627	0.437
SCM	0.568	0.565	0.635	0.599	0.339	0.335	0.560	0.575	0.449	0.550	0.308
KCF	0.543	0.628	0.474	0.580	0.561	0.523	0.613	0.579	0.610	0.630	0.355
Struck	0.491	0.473	0.471	0.493	0.518	0.567	0.528	0.506	0.550	0.545	0.410
TLD	0.460	0.456	0.494	0.468	0.482	0.473	0.476	0.497	0.516	0.388	0.327
ALSA	0.503	0.456	0.544	0.451	0.281	0.260	0.488	0.494	0.359	0.468	0.163
CN	0.450	0.511	0.421	0.479	0.480	0.437	0.550	0.501	0.458	0.531	0.399
VTS	0.503	0.441	0.453	0.465	0.328	0.325	0.477	0.496	0.508	0.516	0.183
VTD	0.480	0.443	0.460	0.468	0.320	0.319	0.500	0.510	0.491	0.515	0.170

Table 3. P_o of the top ten algorithms on eleven attributes

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