Authors:
Marwa Jmal
1
;
Wided Souidene
2
and
Rabah Attia
2
Affiliations:
1
Ecole Polytechnique de Tunisie and Telnet Innovation Labs, Tunisia
;
2
Ecole Polytechnique de Tunisie, Tunisia
Keyword(s):
Local Derivative Patterns, Spatio-temporal Features, Background Modeling, Background Subtraction.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
Abstract:
Nowadays, more attention is being focused on background subtraction methods regarding their importance in many computer vision applications. Most of the proposed approaches are classified as pixel-based due to their low complexity and processing speed. Other methods are considered as spatiotemporal-based as they consider the surroundings of each analyzed pixel.
In this context, we propose a new texture descriptor that is suitable for this task. We benefit from the advantages of local binary patterns variants to introduce a novel spatio-temporal center-symmetric local derivative patterns (STCS-LDP). Several improvements and restrictions are set in the neighboring pixels comparison level, to make the descriptor less sensitive to noise while maintaining robustness to illumination changes. We also present a simple background subtraction algorithm which is based on our STCS-LDP descriptor. Experiments on multiple video sequences proved that our method is efficient and produces comparable
results to the state of the art.
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