Fused discriminative metric learning for low resolution pedestrian detection

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.