Reranking of person re-identification by manifold-based approach

Person re-identification (RE-ID) aims at associating the same pedestrian over non-overlapping surveillance scenes. A large number of approaches have emerged in recent years, and they mainly focus on designing middle or high level features to highlight the most discriminative aspects of pedestrians. Due to the nonrigid structure of pedestrians, it is difficult to re-identify pedestrians by low-level features. We investigate the results of conventional person RE-ID approaches, and find that the inadequate utilization of low-level features lead to the poor performance. In this work, we propose a novel framework to utilize the low-level visual features in a more effective way. Given a result obtained from the conventional person RE-ID method, the framework returns a more reasonable result. The framework is extended from the manifold ranking method, and several adjustments are made taking the requirements of person RE-ID into consideration. Our framework is validated through experiments on two person RE-ID datasets (VIPeR and ETHZ), and results from four different conventional approaches show significant improvement.