Authors:
Bahram Lavi
1
;
Mehdi Fatan Serj
2
and
Domenec Puig Valls
2
Affiliations:
1
University of Cagliari, Italy
;
2
Universitat Rovira i Virgili, Spain
Keyword(s):
Person Re-Identification, Video Surveillance Systems, Dimensional Reduction Methods.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
ICA, PCA, CCA and other Linear Models
;
Image and Video Analysis
;
Kernel Methods
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
;
Video Analysis
Abstract:
One of the goals of person re-identification systems is to support video-surveillance operators and forensic
investigators to find an individual of interest in videos acquired by a network of non-overlapping cameras. This
is attained by sorting images of previously observed individuals for decreasing values of their similarity with
a given probe individual. Existing appearance descriptors, together with their similarity measures, are mostly
aimed at improving ranking quality. Many of these descriptors generate a high feature vector represented
as an image signature. To tackle person re-identification in real-world scenario the processing time will be
crucial, so an individual of interest within a network camera should be found out swiftly. We therefore study
some feature reduction methods to achieve a significant trade-off between processing time and ranking quality.
Although, observing some redundancies on the generated patterns of a given descriptor are not deniable, we
suggest to
employ a feature reduction method before use of it in real-world scenarios. In particular, we have
tested three reduction methods: PCA, KPCA, and Isomap. We then evaluate our study on two benchmark data
sets (VIPeR, and i-LIDS), by using two state-of-the-art descriptors on person re-identification task. The results
presented in this paper, after applying the feature reduction step, are very promising in terms of recognition
rate.
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