Authors:
Du-Ming Tsai
and
Wei-Yao Chiu
Affiliation:
Yuan-Ze University, Taiwan
Keyword(s):
Background Updating, Foreground Segmentation, Object Detection, Mode Estimation.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
In video surveillance, the detection of foreground objects in an image sequence from a still camera is critical for object tracking, activity recognition, and behavior understanding. In this paper, a dual-mode scheme for foreground segmentation is proposed. The mode is based on the most frequently occurring gray level of observed consecutive image frames, and is used to represent the background in the scene. In order to accommodate the dynamic changes of a background, the proposed method uses a dual-mode model for background representation. The dual-mode model can represent two main states of the background and detect a more complete silhouette of the foreground object in the dynamic background. The proposed method can promptly calculate the exact gray-level mode of individual pixels in image sequences by simply dropping the last image frame and adding the current image in an observed period. The comparative evaluation of foreground segmentation methods is performed on the Microsoft’
s Wallflower dataset. The results show that the proposed method can quickly respond to illumination changes and well extract foreground objects in a low-contrast background.
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