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• W2074777933 abstract "This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis. Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known results on the Caltech dataset, reaching 93% recall at 1 FPPI." @default.
• W2074777933 created "2016-06-24" @default.
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• W2074777933 date "2015-06-01" @default.
• W2074777933 modified "2023-10-05" @default.
• W2074777933 title "Filtered channel features for pedestrian detection" @default.
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• W2074777933 doi "https://doi.org/10.1109/cvpr.2015.7298784" @default.
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