Authors:
Vito Renò
1
;
Angelo Cardellicchio
2
;
Tiziano Politi
2
;
Cataldo Guaragnella
2
and
Tiziana D'Orazio
1
Affiliations:
1
Italian National Research Council, Italy
;
2
Politecnico di Bari, Italy
Keyword(s):
Person Re-Identification, Computer Vision, Video Surveillance.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
Abstract:
In this paper, a method to find, exploit and classify ambiguities in the results of a person re-identification (PRID) algorithm is presented. We start from the assumption that ambiguity is implicit in the classical formulation of the re-identification problem, as a specific individual may resemble one or more subjects by the color of dresses or the shape of the body. Therefore, we propose the introduction of the AMbiguity rAte in REidentification (AMARE) approach, which relates the results of a classical PRID pipeline on a specific dataset with their effectiveness in re-identification terms, exploiting the ambiguity rate (AR). As a consequence, the cumulative matching curves (CMC) used to show the results of a PRID algorithm will be filtered according to the AR. The proposed method gives a different interpretation of the output of PRID algorithms, because the CMC curves are processed, split and studied separately. Real experiments demonstrate that the separation of the results is rea
lly helpful in order to better understand the capabilities of a PRID algorithm.
(More)
