Person Reidentification via Multi-Feature Fusion With Adaptive Graph Learning

The goal of person reidentification (Re-ID) is to identify a given pedestrian from a network of non-overlapping surveillance cameras. Existing methods follow the supervised learning paradigm which requires pairwise labeled training data for each pair of cameras. However, this limits their scalability to real-world applications where abundant unlabeled data are available. To address this issue, we propose a multi-feature fusion with adaptive graph learning model for unsupervised ReID. Our model aims to negotiate comprehensive assessment on the consistent graph structure of pedestrians with the help of special information of feature descriptors. Specifically, we incorporate a multi-feature dictionary learning and adaptive multifeatured graph learning into a unified learning model such that the learned dictionaries are discriminative and the subsequent graph structure learning is accurate. An alternating optimization algorithm with proved convergence is developed to solve the final optimization objective

Keywords: Adaptive Graph Learning, Feature Representation Learning, Multi-Feature Fusion, Person Reidentification (Re-ID).