Convolutional Channel Pruning and Weighting for Accurate Location Visual Tracking

Manqiang Che; Shubin Li; Jinpeng Ge

doi:10.3788/LOP57.161505

Laser & Optoelectronics Progress, Volume. 57, Issue 16, 161505(2020)

Convolutional Channel Pruning and Weighting for Accurate Location Visual Tracking

Manqiang Che^*, Shubin Li, and Jinpeng Ge

Unmanned Systems Technology Innovation Center, Guangzhou Haige Communications Group Incorporated Company, Guangzhou, Guangdong 510700, China

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Figures & Tables(7)

Fig. 1. Remained channel features after adaptively channel pruning

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Fig. 2. Correlational filters of channel weighting

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Fig. 3. Results of ten algorithms on OTB-100 dataset. (a) Precision plot; (b) success rate plot

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Fig. 4. Precision of ten algorithms on different attributes

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Fig. 5. Comparison of partial tracking results of eight algorithms at different scenes

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Table 1. Tracking results of different tracking strategies
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Table 1. Tracking results of different tracking strategies
Parameter p4 p4s cp cpg cpgw Ours
Average CLE /pixel 23.6 12.8 12.5 12.2 12.4 12.1
Average DP /% 83.7 87.7 88.8 89.1 90.5 91.3
Average OP /% 70.2 74.6 76.4 77.2 77.9 78.2
Speed /(frame·s^-1) 26.2 33.4 34.0 33.2 32.1 31.8

Table 2. Tracking results of different algorithms on VOT2018 dataset
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Table 2. Tracking results of different algorithms on VOT2018 dataset
Parameter ECO CFCF CFWCR LSART Proposed algorithm
EAO 0.280 0.286 0.303 0.323 0.319
Accuracy 0.483 0.509 0.484 0.493 0.491
Robustness 0.276 0.281 0.267 0.218 0.225

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Manqiang Che, Shubin Li, Jinpeng Ge. Convolutional Channel Pruning and Weighting for Accurate Location Visual Tracking[J]. Laser & Optoelectronics Progress, 2020, 57(16): 161505

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