Hierarchical Convolutional Features for Visual Tracking
Citations
2,936 citations
Cites background or methods from "Hierarchical Convolutional Features..."
...correlation filters) using the network’s internal representation as features [5,6] or perform SGD (stochastic gradient descent) to fine-tune multiple layers of the network [7–9]....
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...[5] and FCNT [8] have achieved strong results, they have been unable to achieve frame-rate operation due to the relatively high dimension of the conv-net representation....
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1,993 citations
Cites background or methods from "Hierarchical Convolutional Features..."
...OTB2015 Dataset: We compare our tracker with 20 stateof-the-art methods: TLD [22], Struck [19], CFLB [16], ACT [13], TGPR [17], KCF [20], DSST [7], SAMF [25], MEEM [38], DAT [33], LCT [28], HCF [27], SRDCF [9], SRDCFad [10], DeepSRDCF [8], Staple [1], MDNet [31], SiameseFC [2], TCNN [30] and C-COT [12]....
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...As mentioned above, the advancement in DCF tracking performance is predominantly attributed to powerful features and sophisticated learning formulations [8, 12, 27]....
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1,613 citations
1,329 citations
1,324 citations
Cites methods from "Hierarchical Convolutional Features..."
...Surprisingly, in the context of tracking, recent DCF-based methods [10,35] have...
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...The DCF-based trackers HCF and Staple obtain AUC scores of 56.6% and 58.4% respectively....
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...[35] employed multiple convolutional layers in a hierarchical ensemble of independent DCF trackers....
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...We validate our Continuous Convolution Operator Tracker (C-COT) in a comprehensive comparison with 20 state-of-the-art methods: ASLA [25], TLD [26], Struck [21], LSHT [22], EDFT [14], DFT [41], CFLB [18], ACT [13], TGPR [19], KCF [24], DSST [9], SAMF [31], MEEM [47], DAT [40], LCT [36], HCF [35], Staple [3] and SRDCF [11]....
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...The HCF tracker, based on hierarchical convolutional features, obtains a mean OP of 65.5%....
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References
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"Hierarchical Convolutional Features..." refers methods in this paper
...The KCF tracker learns a kernelized correlation filter with a Gaussian kernel over HOG features....
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..., HOG [5] or color-attributes [7]) and we construct multiple correlation filters on hierarchical convolutional layers as opposed to only one single filter by existing approaches....
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...The main differences lie in the use of learned CNN features rather than hand-crafted features (e.g., HOG [5] or color-attributes [7]) and we construct multiple correlation filters on hierarchical convolutional layers as opposed to only one single filter by existing approaches....
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21,729 citations