Tracking Algorithm of Correlation Filter with Multiple Features Based on Temporal Consistency and Spatial Pruning

Yixuan Wang; Xiaojun Wu; Tianyang Xu

doi:10.3788/LOP56.221502

Laser & Optoelectronics Progress, Volume. 56, Issue 22, 221502(2019)

Tracking Algorithm of Correlation Filter with Multiple Features Based on Temporal Consistency and Spatial Pruning

Yixuan Wang, Xiaojun Wu^*, and Tianyang Xu

International Joint Laboratory of Pattern Recognition and Computational Intelligence, School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China

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Figures & Tables(10)

Fig. 1. Flow chart of correlation filter tracking algorithm for multiple features based on temporal consistency and spatial pruning

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Fig. 2. Schematic of binary matrix mask

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Fig. 3. Comparison of generated masks by proposed algorithm and CSRDCF algorithm. (a) Mask generated by proposed algorithm based on by single frame image in Matrix video; (b) mask generated by CSRDCF algorithm based on by single frame image in Matrix video; (c) single frame image in Matrix video; (d) mask generated by proposed algorithm based on by single frame image in Basketball video; (e) mask generated by CSRDCF algorithm based on by single frame image in Basketball video; (f) single frame image in

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Fig. 4. Average AUC and average DP curves of 7 algorithms on OTB-100 dataset. (a) Average AUC; (b) average DP

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Fig. 5. Comparison of 9 algorithms on VOT2016 dataset. (a) EAO; (b) AUC

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Fig. 6. Comparison of tracking results of 6 algorithms on Bird1 and Lemming. (a) Bird1; (b) Lemming

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Fig. 7. Comparisonof tracking results of 6 algorithms on Iron Man and Matrix. (a) Iron Man; (b) Matrix

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Table 1. Average AUC results of 6 algorithms in 6 challenges on OTB-100 dataset

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Table 1. Average AUC results of 6 algorithms in 6 challenges on OTB-100 dataset

Algorithm	OCC	BC	DEF	MB	IPR	SV
Ours(D+HC)	0.669	0.694	0.651	0.699	0.658	0.670
ECO-HC	0.614	0.640	0.612	0.629	0.606	0.608
CCOT	0.620	0.613	0.585	0.663	0.582	0.601
BACF	0.556	0.605	0.572	0.570	0.575	0.575
SRDCF	0.554	0.583	0.540	0.590	0.542	0.565
CSRDCF	0.530	0.544	0.531	0.583	0.524	0.528

Table 2. Average DP results of 6 algorithms in 6 challenges on OTB-100 dataset

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Table 2. Average DP results of 6 algorithms in 6 challenges on OTB-100 dataset

Algorithm	OCC	BC	DEF	MB	IPR	SV
Ours(D+HC)	0.869	0.914	0.870	0.888	0.882	0.882
ECO-HC	0.810	0.843	0.825	0.790	0.778	0.803
CCOT	0.851	0.832	0.828	0.844	0.833	0.822
BACF	0.731	0.801	0.766	0.733	0.790	0.773
SRDCF	0.728	0.775	0.731	0.760	0.738	0.747
CSRDCF	0.732	0.756	0.750	0.747	0.748	0.750

Table 3. Tracking results of 9 algorithms on VOT2016 dataset
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Table 3. Tracking results of 9 algorithms on VOT2016 dataset
Performance Ours (D+HC) CCOT TCNN SSAT STAPLE DDC EBT STAPLEp DeepSRDCF
EAO 0.40 0.33 0.32 0.32 0.30 0.29 0.29 0.29 0.28
Failures 8.92 16.58 17.94 19.27 23.90 20.98 15.19 24.32 20.35
AO 0.53 0.47 0.49 0.52 0.39 0.39 0.37 0.39 0.43

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Yixuan Wang, Xiaojun Wu, Tianyang Xu. Tracking Algorithm of Correlation Filter with Multiple Features Based on Temporal Consistency and Spatial Pruning[J]. Laser & Optoelectronics Progress, 2019, 56(22): 221502

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