Learning SURF Cascade for Fast and Accurate Object...
Transcript of Learning SURF Cascade for Fast and Accurate Object...
Learning SURF Cascade for Fast and Accurate Object DetectionSupplementary materials
Jianguo Li, Yimin ZhangIntel Labs China
1. Spatial Cell ConfigurationFigure 1 depicts spatial configuration of SURF patches.
Figure 1. Possible spatial cell configuration of local patches. Eachlocal patch has 4 same-size cells, but these cells may be in differentforms. From left to right, 2×2 cells, 4×1 cells, 1×4 cells.
2. L2HYS Feature NormalizationGiven a feature vector v = (v1, . . . , vd), the L2Hys nor-
malization works like below
(1) L2-normalization: ui = vi/√∥v∥22 + ϵ, where ϵ is
small positive value to avoid dividing by zero;
(2) Clipping ui with the following rule
ui =
θ if ui > θ−θ if ui < −θui otherwise,
where θ = 2/√d empirically 1.
(3) Re-normalization: v′i = ui/√∥u∥22 + ϵ, and v′ =
(v′1, . . . , v′d) is the L2Hys normalization result.
3. Results on UMass FDDBFigure 2 depicts ROC curve from both discrete score and
continuous score on FDDB benchmark by SURF cascade.1After normalization, assuming |ui| < θ, thus
∑u2i < dθ2. ui
can be viewed as samples for a Gaussian variable, the variance σ2 =(∑
u2i )/d = 1/d < θ2. As is known, about 95% samples from the
Gaussian distribution fall within the range [−2σ, 2σ]. Therefore, we de-fine θ = 2σ = 2/
√d.
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SURF_MultiviewSURF_Front_GB
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Viola-Jones [21]Mikolajaczyk et al [25]
Subburaman [33]
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Subburaman [33]
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Figure 2. (a) Discrete score ROC curves and (b) Continuous scoreROC curves for different methods on UMass FDDB dataset.
4. Example Results of Face and Car DetectionFigure 3 depicts some face detection results on FDDB
and CMU+MIT datasets. Figure 4 depicts some car detec-tion results on TUGRAZ dataset.
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Figure 3. Example detection results on CMU+MIT (a) and UMass FDDB datasets (b,c,d).
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Figure 4. Some car detection results on TUGRAZ dataset.