"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Cast shadow segmentation using invariant color features
Computer Vision and Image Understanding
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Probabilistic Fusion of Stereo with Color and Contrast for Bilayer Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Nonchronological Video Synopsis and Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video SnapCut: robust video object cutout using localized classifiers
ACM SIGGRAPH 2009 papers
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Object-based surveillance video retrieval system with real-time indexing methodology
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Extracting foreground moving objects from video sequences is an important task and also a hot topic in computer vision and image processing. Segmentation results can be used in many object-based video applications such as object-based video coding, content-based video retrieval, intelligent video surveillance, video-based human-computer interaction, etc. In this paper, we propose a framework for real-time segmentation of foreground moving objects from monocular video sequences with static background. Our algorithm can extract foreground layers with cast shadow removal accurately and efficiently. To reduce the computation cost, we use Gaussian Mixture Models to model the scene and obtain initial foreground regions. Then we combine the initial foreground mask with shadow detection to generate a quadrant-map for each region. Based on these quadrant-maps, Markov Random Field model is built on each region and the graph cut algorithm is used to get the optimal binary segmentation. To ensure good temporal consistency, we reuse previous segmentation results to build the current foreground model. Experimental results on various videos demonstrate the efficiency of our proposed method.