Authors:
Naima Mubariz
;
Saba Mumtaz
;
M. M. Hamayun
and
M. M. Fraz
Affiliation:
National University of Sciences and Technology, Pakistan
Keyword(s):
Public Safety and Security (PSS), Person Re-identification, Metric Learning, Visual Surveillance, Biometrics.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
Person re-identification is a complex computer vision task which provides authorities a valuable tool for
maintaining high level security. In surveillance applications, human appearance is considered critical since
it possesses high discriminating power. Many re-identification algorithms have been introduced that employ a
combination of visual features which solve one particular challenge of re-identification. This paper presents a
new type of feature descriptor which incorporates multiple recently introduced visual feature representations
such as Gaussian of Gaussian (GOG) andWeighted Histograms of Overlapping Stripes (WHOS) latest version
into a single descriptor. Both these feature types demonstrate complementary properties that creates greater
overall robustness to re-identification challenges such as variations in lighting, pose, background etc. The new
descriptor is evaluated on several benchmark datasets such as VIPeR, CAVIAR4REID, GRID, 3DPeS, iLIDS,
ETHZ1 and PRID45
0s and compared with several state-of-the-art methods to demonstrate effectiveness of the
proposed approach.
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