Authors:
Fouad bousetouane
1
;
Cina Motamed
2
and
Lynda Dib
1
Affiliations:
1
Badji Mokhtar University, Algeria
;
2
LISIC Laboratory, France
Keyword(s):
Inter-camera Re-identification, Non-overlapping Views, Distributed Inferences, Low-level Contextual Cues, Brightness Transfer Function, Consensus-based Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Pervasive Smart Cameras
;
Tracking and Visual Navigation
;
Video Surveillance and Event Detection
Abstract:
Multi-object re-identification across cameras network with non-overlapping fields of view is a challenging
problem. Firstly, the visual signature of the same object might be very different from one camera to another.
Secondly, the blind zone between cameras creates the discontinuity in the observation of the same
object in terms of locations and travelling times. Centralized inferences proposed in literature for inter-camera
re-identification becomes insufficient in practice mostly with the requirement of real-time applications and
dynamic cameras network. In this paper we present a completely distributed approach for inter-camera reidentification.
The proposed approach based on the distributed inferences, where the set of smart-cameras
collaborate to reach a consensus about the identities of objects circulating in the network. Local and global
visual descriptors were combined into the proposed approach for inter-camera color mapping and invariant
objects description. Experimental re
sults of applying this approach show improvement in inter-camera reidentification
and robustness in recovering from very complex conditions.
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