Common-near-neighbor analysis for person re-identification

Person re-identification tackles the problem whether an observed 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 scenarios: illumination, pose and viewpoint changes across cameras...

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Bibliographic Details
Published in2012 19th IEEE International Conference on Image Processing pp. 1621 - 1624
Main Authors Wei Li, Yang Wu, Mukunoki, M., Minoh, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
Cameras
common-nearneighbor analysis
Educational institutions
Extraterrestrial measurements
Lighting
metric learning
Person re-identification
Support vector machines
Surveillance
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Summary:Person re-identification tackles the problem whether an observed 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 scenarios: illumination, pose and viewpoint changes across cameras. So far, proposals in the literature have treated this either as a matching problem focusing on feature representation 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 commonness 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 tested on widely-used benchmark datasets, showing significant performance improvement over the state-of-the-art.
ISBN:1467325341
9781467325349
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2012.6467186