Asymmetric person re-identification: cross-view person tracking in a large camera network
Ancong Wu, Wei‐Shi Zheng · Scientia Sinica Informationis · 2018
Person re-identification (RE-ID) is critical and crucial for tracking people across multiple camera views, and hence, the piece-wise tracklets of each person from different locations can be connected. In this paper, we first review the development of person RE-ID and present its challenges. Subsequently, we introduce our recent development on asymmetric distance metric learning and the asymmetric person RE-ID modeling of the largely unsolved open-topic problems. Existing metric learning methods for person RE-ID usually ignore the characteristics of feature transformations between different camera views. The advantage of asymmetric metric is that it can model inconsistent feature transformations between different camera views. Except for being applied to general person RE-ID problem, asymmetric model can also be applied to cross-modality RE-ID, low-resolution RE-ID, attribute-image RE-ID, unsupervised RE-ID and partial RE-ID. Finally, we discuss the future development of person RE-ID.