Adaptive Correlation Filtering Tracking Algorithm for Complex Scenes

Mingrui Lu; Chao Han; Fan Lu; Baorui Miao; Jikun Yang; Junjun Zha; Wenhan Sha

doi:10.3788/LOP202259.2415004

Laser & Optoelectronics Progress, Volume. 59, Issue 24, 2415004(2022)

Adaptive Correlation Filtering Tracking Algorithm for Complex Scenes

Mingrui Lu^1,2, Chao Han^1,2、*, Fan Lu^1,2, Baorui Miao^1,2, Jikun Yang^1,2, Junjun Zha^1,2, and Wenhan Sha³

Author Affiliations

¹School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, Anhui , China

²Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, Anhui , China

³Chery New Energy Automobile Co., Ltd., Wuhu 241000, Anhui , China

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Figures & Tables(16)

Fig. 1. Response diagrams corresponding to Joggle-1 sequence. (a) No occlusion image in frame 4; (b) response diagram of frame 4;(c) occlusion image in frame 49; (d) response diagram of frame 49

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Fig. 2. Flowchart of the proposed algorithm

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Fig. 3. Influence of different parameters on tracker performance. (a) Parameter ζ; (b) parameter τ; (c) parameters η1 and η2

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Fig. 4. Precision and success rate of different feature weighted methods. (a) Precision; (b) success rate

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Fig. 5. Scale changes of different algorithms under three groups of different video sequences. (a) Blurcar2; (b) Doll; (c) Carscale

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Fig. 6. ΔEAPC value and changes of each frame under the Joggle sequences. (a) Joggle-1; (b) Joggle-2

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Fig. 7. CLE changing between two sets of videos. (a) Basketball; (b) Faceocc1

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Fig. 8. Precision and success rate of seven algorithms on OTB50 dataset. (a) Precision; (b) success rate

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Fig. 9. Precision and success rate of seven algorithms on OTB2015 dataset. (a) Precision; (b) success rate

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Fig. 10. Comparison of seven algorithms on different video sequences. (a) Box; (b) Dragonbaby; (c) Bird2; (d) Panda; (e) Carscale; (f) Soccer; (g) Tiger2

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Table 1. Three weighted ratios of multi-feature fusion
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Table 1. Three weighted ratios of multi-feature fusion
Parameter $α$ $β$ $γ$
$λ$ ₁ 0.5 0.4 0.1
$λ$ ₂ 0.3 0.6 0.1
$λ$ ₃ 1/3 1/3 1/3

Table 2. Index of the proposed algorithm under three groups of scale sequences
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Table 2. Index of the proposed algorithm under three groups of scale sequences
Sequence Precision Success rate Speed /（frame·s^-1）
Blurcar2 0.999 1.000 44.06
Carscale 0.897 0.980 60.42
Doll 0.961 0.966 57.55

Table 3. Result comparison of search area algorithms
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Table 3. Result comparison of search area algorithms
Parameter OURS OURS4 OURS5 OURS6 SRDCF SRDCFdecon SAMF
Success rate 0.674 0.564 0.626 0.656 0.634 0.677 0.576
Speed /（frame·s^-1） 39.07 52.10 44.24 35.52 2.05 5.18 14.54

Table 4. Precision of seven target tracking algorithms at 11 different challenge attributes

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Table 4. Precision of seven target tracking algorithms at 11 different challenge attributes

Algorithm	IV	DEF	SV	OCC	MB	FM	IPR	OPR	OV	BC	LR
OURS	0.760	0.741	0.755	0.764	0.787	0.732	0.805	0.790	0.630	0.808	0.707
SRDCFdecon	0.803	0.730	0.771	0.734	0.808	0.763	0.715	0.759	0.561	0.814	0.600
SRDCF	0.715	0.693	0.689	0.673	0.734	0.744	0.631	0.675	0.520	0.676	0.594
SAMF	0.648	0.650	0.663	0.706	0.674	0.657	0.665	0.701	0.605	0.630	0.645
fDSST	0.715	0.589	0.628	0.612	0.658	0.660	0.680	0.617	0.476	0.720	0.609
DSST	0.677	0.509	0.578	0.552	0.585	0.548	0.644	0.598	0.357	0.648	0.510
KCF	0.676	0.573	0.591	0.603	0.622	0.614	0.651	0.624	0.428	0.677	0.538

Table 5. Success rate of seven target tracking algorithms at 11 different challenge attributes

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Table 5. Success rate of seven target tracking algorithms at 11 different challenge attributes

Algorithm	IV	DEF	SV	OCC	MB	FM	IPR	OPR	OV	BC	LR
OURS	0.692	0.667	0.633	0.687	0.742	0.683	0.688	0.693	0.531	0.720	0.501
SRDCFdecon	0.748	0.641	0.712	0.694	0.795	0.729	0.649	0.686	0.561	0.740	0.571
SRDCF	0.675	0.626	0.631	0.640	0.719	0.712	0.574	0.607	0.492	0.634	0.581
SAMF	0.584	0.555	0.562	0.641	0.660	0.598	0.602	0.622	0.490	0.596	0.539
fDSST	0.645	0.525	0.547	0.553	0.633	0.641	0.610	0.545	0.438	0.645	0.547
DSST	0.601	0.437	0.489	0.491	0.568	0.515	0.552	0.506	0.305	0.535	0.423
KCF	0.503	0.434	0.396	0.475	0.565	0.534	0.515	0.477	0.376	0.576	0.325

Table 6. Running speed of seven target tracking algorithms
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Table 6. Running speed of seven target tracking algorithms
Algorithm Speed （frame·s^-1）
OTB50 OTB2015
OURS 39.0778 39.2448
SRDCFdecon 2.0497 1.9694
SRDCF 5.1833 4.871
SAMF 14.5473 13.933
fDSST 37.4683 31.6959
DSST 8.4053 7.0361
KCF 198.4457 173.5403

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Mingrui Lu, Chao Han, Fan Lu, Baorui Miao, Jikun Yang, Junjun Zha, Wenhan Sha. Adaptive Correlation Filtering Tracking Algorithm for Complex Scenes[J]. Laser & Optoelectronics Progress, 2022, 59(24): 2415004

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