COMMON-NEAR-NEIGHBOR ANALYSIS FOR PERSON RE-IDENTIFICATION

Person re-identification tackles the problem whether an ob-served person of interest reappears in a network of cameras. The difficulty primarily originates from few samples per class but large amounts of intra-class variations in real s-cenarios: illumination, pose and viewpoint changes across cameras. So far, proposals in the literature have treated this either as a matching problem focusing on feature representa-tion or as a classification/ranking problem relying on metric optimization. This paper presents a new way called Common-Near-Neighbor Analysis, which to some extent combines the strengths of these two methodologies. It analyzes the com-monness of the near neighbors of each pair of samples in a learned metric space, measured by a novel rank-order based dissimilarity. Our method, using only color cue, has been test-ed on widely-used benchmark datasets, showing significant performance improvement over the state-of-the-art. Index Terms — Person re-identification, common-near-neighbor analysis, metric learnin