Scale-Adaptive Correlation Filter Tracking Algorithm Based on FHOG and LBP Features

Xiaoyue Liu; Yunming Wang; Weining Ma

doi:10.3788/LOP57.041512

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 4 >Page 041512 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 4, 041512 (2020)

Scale-Adaptive Correlation Filter Tracking Algorithm Based on FHOG and LBP Features

Xiaoyue Liu^*, Yunming Wang, and Weining Ma

Author Affiliations

College of Electrical Engineering, North China University of Science and Technology, Tangshan, Hebei 063210, China

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DOI: 10.3788/LOP57.041512 Cite this Article Set citation alerts

Xiaoyue Liu, Yunming Wang, Weining Ma. Scale-Adaptive Correlation Filter Tracking Algorithm Based on FHOG and LBP Features[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041512 Copy Citation Text

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Fig. 1. Schematic of FHOG feature extraction

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Fig. 2. Extraction principle of LBP feature

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Fig. 3. Schematic of target tracking and scale estimation for the current frame

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Fig. 4. Comparison of accuracy rate

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Fig. 5. Comparison of success rate

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Fig. 6. Diagrams of partial tracking effects of 6 algorithms

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Image sequence	Number of frames	Size /(pixel×pixel)	Property description
Bird1	408	720×400	DEF, FM, OV
Box	1161	640×480	IV, SV, OCC, MB, IPR, OPR, OV, BC, LR
Skating2	473	640×352	SV, OCC, DEF, FM, OPR
Basketball	725	576×432	IV, OCC, DEF, OPR, BC
Soccer	392	640×360	IV, SV, OCC, MB, FM, IPR, OPR, BC
CarScale	252	640×272	SV, OCC, FM, IPR, OPR

Table 1. Information description of the selected image sequence

Algorithm parameter	Value
Regularization parameter λ	10^-4
Ratio of searching areas	1.6
Learning ratio of apparent model	[0.01,0.015]
Number of directions of FHOG	9
Cell size of FHOG /(pixel×pixel)	4×4
Learning rate η	0.01
Scale factor a	1.02
Scale space N	33

Table 2. Main parameters of the algorithm in this paper

Imagesequence	CLE /pixel
Imagesequence	LCT	Staple	Struck	CSK	KCF	Our
Bird1	6.925	4.599	8.498	6.031	9.778	4.375
Box	5.984	6.721	6.196	5.633	6.756	5.376
Skating2	6.969	3.095	3.065	2.606	5.189	2.482
Basketball	9.765	7.386	7.352	7.269	12.889	7.198
Soccer	4.033	4.93	4.356	4.017	3.997	3.949
CarScale	6.927	6.511	6.281	5.975	10.684	5.918

Table 3. CLE of six algorithms on six sets of test image sequences

Imagesequence	DP /%
Imagesequence	LCT	Staple	Struck	CSK	KCF	Our
Bird1	100.00	34.90	36.50	35.20	95.00	100.00
Box	92.80	86.40	87.20	91.60	86.40	100.00
Skating2	100.00	68.90	79.10	78.90	100.00	100.00
Basketball	24.60	24.00	24.00	24.00	100.00	100.00
Soccer	69.40	96.70	67.90	17.90	79.30	66.60
CarScale	75.80	75.20	69.40	65.10	80.60	82.10
Mean	77.10	64.35	60.68	51.17	90.22	91.45

Table 4. Comparison of DP data of different algorithms

Imagesequence	OP /%
Imagesequence	LCT	Staple	Struck	CSK	KCF	Our
Bird1	100.00	27.60	26.80	27.60	36.40	100.00
Box	30.60	46.40	52.30	63.20	74.20	62.80
Skating2	97.70	65.90	67.90	53.30	84.00	99.80
Basketball	22.30	22.30	22.20	22.30	100.00	97.70
Soccer	39.00	48.20	36.70	16.30	39.30	20.70
CarScale	84.50	44.80	44.60	44.80	44.40	93.70
Mean	62.35	42.53	41.75	37.92	63.05	79.17

Table 5. Comparison of OP data of different algorithms

Xiaoyue Liu, Yunming Wang, Weining Ma. Scale-Adaptive Correlation Filter Tracking Algorithm Based on FHOG and LBP Features[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041512

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