Foreground background segmentation in front of changing footage on a video screen

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 F1-scores ranging from 85.61% to 90.74% on our dataset.

ISBN:9783030014490

Publication year:2018

BOF-keylabel:yes

IOF-keylabel:yes

Accessibility:Closed