Abstract
Person re-identification (Re-ID) remains a challenging
problem due to significant appearance changes caused by
variations in view angle, background clutter, illumination
condition and mutual occlusion. To address these issues,
conventional methods usually focus on proposing robust
feature representation or learning metric transformation
based on pairwise similarity, using Fisher-type criterion.
The recent development in deep learning based approaches address the two processes in a joint fashion and have
achieved promising progress. One of the key issues for deep
learning based person Re-ID is the selection of proper similarity comparison criteria, and the performance of learned
features using existing criterion based on pairwise similarity is still limited, because only Point to Point (P2P) distances are mostly considered. In this paper, we present a
novel person Re-ID method based on Point to Set similarity comparison. The Point to Set (P2S) metric can jointly
minimize the intra-class distance and maximize the interclass distance, while back-propagating the gradient to optimize parameters of the deep model. By utilizing our proposed P2S metric, the learned deep model can effectively
distinguish different persons by learning discriminative and
stable feature representations. Comprehensive experimental evaluations on 3DPeS, CUHK01, PRID2011 and Market1501 datasets demonstrate the advantages of our method
over the state-of-the-art approaches