High-Resolution and Low-Resolution Video Person Re-Identification: A Benchmark

Person re-identification has recently attracted increasing interest in the computer vision and safety-critical applications. In practice, due to poor quality of cameras or long distance away from person, the captured pedestrian videos usually suffer from low resolution, which will result in the loss of useful information contained in videos and make person re-identification between low-resolution (LR) and high-resolution (HR) videos (PRLHV) be a challenging task. However, the problem of PRLHV has not been well studied. In this paper, we propose a semi-coupled mapping based set-to-set distance learning (SMDL) approach for PRLHV. Specifically, by regarding each video as a set of features extracted from several walking cycles, we learn a discriminative set-to-set distance metric to enhance the separability between videos from different persons. To decrease the influence of low resolution on the distance learning, we design a clustering-based semi-coupled mapping term for our approach, which can reduce the variation between features of low-resolution and high-resolution videos by a semi-coupled mapping matrix. Since there exists no low-resolution video pedestrian re-identification dataset under real-world scenario up to now, we contribute a benchmark dataset for PRLHV, named high-resolution and low-resolution video person re-identification dataset (HLVID). Although this dataset is challenging and difficult for person re-identification, it is a useful attempt for further studies on low-resolution video-based pedestrian re-identification under the real-world scene. The extensive experiments on the newly collected video dataset demonstrate that our approach performs better than the state-of-the-art person re-identification methods in the PRLHV task.