Statistical model-based change detection in moving video
Signal Processing
SIAM Journal on Numerical Analysis
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting Simply Connected Moving Objects in a Static Scene
IEEE Transactions on Pattern Analysis and Machine Intelligence
The MPEG-4 video standard verification model
IEEE Transactions on Circuits and Systems for Video Technology
Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework
IEEE Transactions on Circuits and Systems for Video Technology
Region-based representations of image and video: segmentation tools for multimedia services
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
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The algorithm presented in this paper is comprised of three main stages: (1) classification of the image sequence and, in the case of a moving camera, parametric motion estimation, (2) change detection having as reference a fixed frame, an appropriately selected frame or a displaced frame, and (3) object localization using local colour features. The image sequence classification is based on statistical tests on the frame difference. The change detection module uses a two-label fast marching algorithm. Finally, the object localization uses a region growing algorithm based on the colour similarity. Video object segmentation results are shown using the COST 211 data set.