Low-resolution pedestrian detection via a novel resolution-score discriminative surface

Pedestrian detection, as an important task in video surveillance and forensics applications, has been widely studied. However, its performance is unsatisfactory especially in the low resolution conditions. In realistic scenarios, the size of pedestrians in the images is often small, and detection can be challenging. To solve this problem, this paper proposes a novel resolution-score discriminative surface method to investigate the variation behaviors of detection scores under different pedestrian and non-pedestrian image resolutions. The discriminative surface consists of a series of positive and negative resolution-score lines, and each of them is a connected line to depict the variation relationship between pedestrian's detection scores under various image resolutions. On this basis, the resolution-score discriminative surface can classify a resolution-score line as a pedestrian or not according to whether it lies in the positive or the negative region. Experimental results on two public datasets and one campus surveillance dataset demonstrate the effectiveness of the proposed method.