Markov random field modeling in image analysis
Markov random field modeling in image analysis
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nichtlineare Bayes-Restauration mittels eines verallgemeinerten Gauß-Markov-Modells
Mustererkennung 1999, 21. DAGM-Symposium
Video Analysis for Universal Multimedia Messaging
SSIAI '02 Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
Conceptual structures and computational methods for indexing and organization of visual information
Conceptual structures and computational methods for indexing and organization of visual information
Simultaneous motion estimation and segmentation
IEEE Transactions on Image Processing
Semiautomatic segmentation and tracking of semantic video objects
IEEE Transactions on Circuits and Systems for Video Technology
Moving object tracking in H.264/AVC bitstream
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
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This contribution presents a statistical method for segmentation and tracking of moving regions from the compressed videos. This technique is particularly efficient to analyse and track motion segments from the compression-oriented motion fields by using the Bayesian estimation framework. For each motion field, the algorithm initialises a partition that is subject to comparisons and associations with its tracking counterpart. Due to potential hypothesis incompatibility, the algorithm applies a conflict resolution technique to ensure that the partition inherits relevant characteristics from both hypotheses as far as possible. Each tracked region is further classified as a background or a foreground object based on an approximation of the logical mass, momentum, and impulse. The experiment has demonstrated promising results based on standard test sequences.