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- W2024886967 abstract "Cascaded object detectors have demonstrated great success in fast object detection, where image regions can quickly be rejected using a cascade of increasingly complex rejectors/detectors. Although such cascaded detectors typically are fast and require minimal computation, they usually require iterative training, where classifiers are retrained to optimize rejection thresholds after testing on a validation set. We propose a cascaded object detector that uses probabilistic resampling for boosting reweighting, which has the advantage that only a single training step is required. Decision thresholds can be tuned on a validation set without the need for classifier retraining. Empirical results on a pedestrian detection task demonstrate that this reweighting results in a strong classifier that quickly rejects image regions and offers higher accuracy than other competing approaches." @default.
- W2024886967 created "2016-06-24" @default.
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- W2024886967 date "2011-12-01" @default.
- W2024886967 modified "2023-09-25" @default.
- W2024886967 title "REBoost: probabilistic resampling for boosted pedestrian detection" @default.
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- W2024886967 doi "https://doi.org/10.1117/1.3658762" @default.
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