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- W2169671170 abstract "In this paper, we address the problem of detecting pedestrians in still images. We introduce an algorithm for learning shapelet features, a set of mid-level features. These features are focused on local regions of the image and are built from low-level gradient information that discriminates between pedestrian and non-pedestrian classes. Using Ad-aBoost, these shapelet features are created as a combination of oriented gradient responses. To train the final classifier, we use AdaBoost for a second time to select a subset of our learned shapelets. By first focusing locally on smaller feature sets, our algorithm attempts to harvest more useful information than by examining all the low-level features together. We present quantitative results demonstrating the effectiveness of our algorithm. In particular, we obtain an error rate 14 percentage points lower (at 10 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>-6</sup> FPPW) than the previous state of the art detector of Dalal and Triggs on the INRIA dataset." @default.
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- W2169671170 date "2007-06-01" @default.
- W2169671170 modified "2023-10-18" @default.
- W2169671170 title "Detecting Pedestrians by Learning Shapelet Features" @default.
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- W2169671170 doi "https://doi.org/10.1109/cvpr.2007.383134" @default.
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