Authors:
Etienne Corvee
;
Slawomir Bak
and
François Brémond
Affiliation:
INRIA, France
Keyword(s):
People Detection, People Tracking, People Re-identification, Local Binary Pattern, Mean Riemannian Covariance.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image and Video Coding and Compression
;
Image Formation and Preprocessing
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
Re-identifying people in a network of non overlapping cameras requires people to be accurately detected and tracked in order to build a strong visual signature of people appearances. Traditional surveillance cameras do not provide high enough image resolution to iris recognition algorithms. State of the art face recognition can not be easily applied to surveillance videos as people need to be facing the camera at a close range. The different lighting environment contained in each camera scene and the strong illumination variability occurring as people walk throughout a scene induce great variability in their appearance. In addition, people images occlud each other onto the image plane making people detection difficult to achieve. We propose a novel simplified Local Binary Pattern features to detect people, head and faces. A Mean Riemannian Covariance Grid (MRCG) is used to model appearance of tracked people to obtain highly discriminative human signature. The methods are evaluated an
d compared with the state of the art algorithms. We have created a new dataset from a network of 2 cameras showing the usefulness of our system to detect, track and re-identify people using appearance and face features.
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