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Matching individuals within a group of spatially nonoverlapping surveillance cameras, also known as person reidentification, has recently attracted a lot of research interest. Current methods mainly focus on feature representation or distance measure, which directly compare person images captured by different cameras. However, it is still a problem because of various surveillance conditions; for example,...
This paper proposes a simple but efficient bidirectional ranking method to improve person re-identification results across non-overlapping cameras. Previous methods treat person reidentification as a special object retrieval problem, and compute the final rank result purely based on a unidirectional matching between the probe and all gallery images. However, the expected person image may be excluded...
Matching individuals across a group of spatially non-overlapping surveillance cameras, also known as person re-identification, has recently attracted a lot of research interests. Current methods mainly focus on feature extraction or metric learning, which directly compare person images captured by different cameras, but seldom consider device differences caused by various surveillance conditions,...
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