AttRel: An Approach to Person Re-Identification by Exploiting Attribute Relationships
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Person Re-Identification refers to recognizing people across cameras with non-overlapping capture areas. To recognize people, their images must be represented by feature vectors for matching. Recent state-of-the-art approaches employ semantic features, also known as attributes (e.g. wearing-bags, jeans, skirt), for presentation. However, such presentations are sensitive to attribute detection results which can be irrelevant due to noise. In this paper, we propose an approach to exploit relationships between attributes for refining attribute detection results. Experimental results on benchmark datasets (VIPeR and PRID) demonstrate the effectiveness of our proposed approach.
KeywordsPerson Re-Identification Attribute Relationships Re-Score Learning Relationships
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