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• W2773687361 abstract "This paper presents a fully convolutional architecture for pedestrian detection. The DenseNet model is incorporated in the Faster R-CNN framework to extract the deep convolutional features. A two-phase approach is suggested to minimize the false positives owing to hard negative backgrounds. Feature maps from multiple intermediate layers are taken into consideration to facilitate small-scale detection. The proposed method alongside few competent schemes are compared on two benchmark datasets. The obtained results demonstrate the potential of our approach in addressing the real world challenges." @default.
• W2773687361 created "2017-12-22" @default.
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• W2773687361 date "2017-09-01" @default.
• W2773687361 modified "2023-10-14" @default.
• W2773687361 title "Faster R-CNN with densenet for scale aware pedestrian detection vis-à-vis hard negative suppression" @default.
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• W2773687361 doi "https://doi.org/10.1109/mlsp.2017.8168128" @default.
• W2773687361 hasPublicationYear "2017" @default.
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