Authors:
Umar Iqbal
1
;
Igor D. D. Curcio
2
and
Moncef Gabbouj
1
Affiliations:
1
Tampere University of Technology, Finland
;
2
Nokia Research Center, Finland
Keyword(s):
Semi-supervised Person Re-identification, Important Person Detection, Face Tracks, Clustering.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Multimedia Forensics
Abstract:
Given a crowd-sourced set of videos of a crowded public event, this paper addresses the problem of detecting
and re-identifying all appearances of every individual in the scene. The persons are ranked according to
the frequency of their appearance and the rank of a person is considered as the measure of his/her importance.
Grouping appearances of every person from such videos is a very challenging task. This is due to unavailability
of prior information or training data, large changes in illumination, huge variations in camera viewpoints,
severe occlusions and videos from different photographers. These problems are made tractable by exploiting
a variety of visual and contextual cues i.e., appearance, sensor data and co-occurrence of people. A unified
framework is proposed for efficient person matching across videos followed by their ranking. Experimental
results on two challenging video data sets demonstrate the effectiveness of the proposed algorithm.