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- W2891947006 abstract "Low resolution (LR) is one of the most challenging factor in pedestrian detection. In this paper, we propose a fused discriminative metric learning (F-DML) approach for low resolution pedestrian detection without explicit super resolution. We firstly learn a discriminative high resolution (HR) feature space as target space. Then, an optimal Mahanalobis metric is learned to transform the LR feature space into a new LR classification space, which largely preserves the discriminative structure of the HR feature space. Finally, a weighted K-nearest neighbors classifier is applied in the LR classification space which inherits good discrimination from HR feature space. A new training strategy is proposed to find the fewest and most representative LR-HR exemplars. In addition, we build a new dataset for the evaluation of low resolution pedestrian detection methods. Extensive experimental results demonstrate that the proposed approach performs favorably against the state-of-the-art methods." @default.
- W2891947006 created "2018-09-27" @default.
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- W2891947006 date "2018-10-01" @default.
- W2891947006 modified "2023-09-28" @default.
- W2891947006 title "Fused Discriminative Metric Learning for Low Resolution Pedestrian Detection" @default.
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- W2891947006 doi "https://doi.org/10.1109/icip.2018.8451791" @default.
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