Person re-identification by modelling principal component analysis coefficients of image dissimilarities

Signature-based matching has been the dominant choice for state-of-the-art person re-identification across multiple disjoint cameras. An approach that exploits image dissimilarities is proposed, treating re-identification as a binary classification problem. To achieve the objective, the person re-id...

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Veröffentlicht in:	Electronics letters Jg. 50; H. 14; S. 1000 - 1001
Hauptverfasser:	Martinel, N, Micheloni, C
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Stevenage The Institution of Engineering and Technology 03.07.2014 Institution of Engineering and Technology John Wiley & Sons, Inc
Schlagworte:	Applied sciences Artificial intelligence Benchmarking binary classification problem Cameras Classification Computer science; control theory; systems Data processing. List processing. Character string processing Exact sciences and technology Image acquisition Image and vision processing and display technology image classification image dissimilarities image matching image sensors linear subspace Memory organisation. Data processing multiple disjoint cameras Pattern recognition. Digital image processing. Computational geometry PCA coefficients modelling person reidentification principal component analysis signature‐based matching Software State of the art Subspaces Trains image sensors signature-based matching binary classification problem image classification PCA coefficients modelling person reidentification image dissimilarities linear subspace multiple disjoint cameras image matching principal component analysis Multiple view Computer vision Vector space Similarity Surveillance Experimental study Modeling Binary classification Principal component analysis
ISSN:	0013-5194, 1350-911X, 1350-911X
Online-Zugang:	Volltext
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