Title: Foreground Background Segmentation in Front of Changing Footage on a Video Screen
Abstract: In this paper, a robust approach for detecting foreground objects moving in front of a video screen is presented. The proposed method constructs a background model for every image shown on the screen, assuming these images are known up to an appearance transformation. This transformation is guided by a color mapping function, constructed in the beginning of the sequence. The foreground object is then segmented at runtime by comparing the input from the camera with a color mapped representation of the background image, by analysing both direct color and edge feature differences. The method is tested on challenging sequences, where the background screen displays photo-realistic videos. It is shown that the proposed method is able to produce accurate foreground masks, with obtained $$F_1$$ -scores ranging from 85.61% to 90.74% on our dataset.
Publication Year: 2018
Publication Date: 2018-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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