The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Person Reidentification Using Spatiotemporal Appearance
Abstract
In many surveillance applications it is desirable to determine if a given individual has been previously observed over a network of cameras. This is the person reidentification problem. This paper focuses on reidentification algorithms that use the overall appearance of an individual as opposed to passive biometrics such as face and gait. Person reidentification approaches have two aspects: (i) establish correspondence between parts, and (ii) generate signatures that are invariant to variations in illumination, pose, and the dynamic appearance of clothing. A novel spatiotemporal segmentation algorithm is employed to generate salient edgels that are robust to changes in appearance of clothing. The invariant signatures are generated by combining normalized color and salient edgel histograms. Two approaches are proposed to generate correspondences: (i) a model based approach that fits an articulated model to each individual to establish a correspondence map, and (ii) an interest point operator approach that nominates a large number of potential correspondences which are evaluated using a region growing scheme. Finally, the approaches are evaluated on a 44 person database across 3 disparate views.
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Custom Pictorial Structures for Re-identification
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Human Reidentification with Transferred Metric Learning
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Learning to rank in person re-identification with metric ensembles
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Metadata
- Authors
- Gheissari, N., Sebastian, T. B., & Hartley, R.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 2
- Pages
- 1528-1535
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2131255818
- Cited by
- 552
- References
- 23
- FWCI
- 7.35
- Type
- conference paper
- Language
- en
- Open access
- closed
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