Authors:
Paolo Spagnolo
;
Tiziana D’Orazio
;
Marco Leo
;
Nicola Mosca
and
Massimiliano Nitti
Affiliation:
Istituto di Studi sui Sistemi Intelligenti per l’Automazione - CNR, Italy
Keyword(s):
Motion Detection, Background Subtraction, Background Modeling.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Detecting 3D Objects Using Patterns of Motion and Appearance
;
Human-Computer Interaction
;
Image and Video Analysis
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Segmentation and Grouping
;
Software Engineering
;
Video Analysis
Abstract:
Detecting moving objects is very important in many application contexts such as people detection, visual surveillance, automatic generation of video effects, and so on. The first and fundamental step of all motion detection algorithms is the background modeling. The goal of the methodology here proposed is to create a background model substantially independent from each hypothesis about the training phase, as the presence of moving persons, moving background objects, and changing (sudden or gradual) light conditions. We propose an unsupervised approach that combines the results of temporal analysis of pixel intensity with a sliding window procedure to preserve the model from the presence of foreground moving objects during the building phase. Moreover, a multilayered approach has been implemented to handle small movements in background objects. The algorithm has been tested in many different contexts, in both indoor and outdoor environments. Finally, it has been tested even on the CA
VIAR 2005 dataset.
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