Low illumination person re-identification

Low illumination is a common problem for recognition and tracking. Low illumination video-based person re identification (re-id) is an important application in practice. Low illumination usually results in severe loss of visual appearance and space-time information contained in pedestrian image or video, which brings large difficulty to re-identification. However, the problem of low illumination video-based person re-id (LIVPR) has not been well studied. In this paper, we propose a novel triplet-based manifold discriminative distance learning (TMD2L) approach for LIVPR. By regarding each video as an image set, TMD2L aims to learn a manifold-based distance metric, under which the intrinsic structure of image sets can be preserved, and the distance between truly matching sets is smaller than that between wrong matching sets. Experiment results on the new collected low illumination person sequence (LIPS) dataset, as well as two simulated datasets LI-PRID 2011 and LI-iLIDS-VID show that our proposed approach TMD2L outperforms existing representative person re-id methods.