@inproceedings{a158efeabd6d48b6a2f96ba49d550685,
title = "Fused discriminative metric learning for low resolution pedestrian detection",
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.",
keywords = "Low resolution, Metric learning, Pedestrian detection",
author = "Xinzhao Li and Yuehu Liu and Zeqi Chen and Jiahuan Zhou and Ying Wu",
year = "2018",
month = aug,
day = "29",
doi = "10.1109/ICIP.2018.8451791",
language = "English (US)",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "958--962",
booktitle = "2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings",
address = "United States",
note = "25th IEEE International Conference on Image Processing, ICIP 2018 ; Conference date: 07-10-2018 Through 10-10-2018",
}