Authors:
Aziz Dziri
1
;
Marc Duranton
1
and
Roland Chapuis
2
Affiliations:
1
CEA and LIST, France
;
2
Pascal Blaise University, France
Keyword(s):
GMPHD, Occlusion, Overlapping, Multi-object Tracking, Background Subtraction.
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
;
Tracking and Visual Navigation
;
Video Surveillance and Event Detection
Abstract:
In this paper, we propose a vision tracking system primarily targeted for systems with low computing resources.
It is based on GMPHD filter and can deal with occlusion between objects. The proposed algorithm
is supposed to work in a node of camera network where the cost of the computer processing the information
is critical. To achieve a low computing complexity, a basic background subtraction algorithm combined with
a connected component analysis method are used to detect the objects of interest. GMPHD was improved
to detect occlusions between objects and to handle their identities once the occlusion ends. The occlusion
is detected using a low complexity distance criterion that takes into consideration the object’s bounding box.
When an occlusion is noticed, the features of the overlapped objects are saved. At the end of the overlapping,
the extracted features are compared to the current features of the objects to perform the object reidentification.
In our experiments two different
features are tested: color histogram features and motion features. The experiments
are performed on two datasets: PETS2009 and CAVIAR. The obtained results show that our approach
ensures a high improvement of GMPHD filter and has a low computing complexity.
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