Segmentation and tracking of moving objects for content-based video coding

To enable content-based functionalities in video coding, a decomposition of the scene into physical objects is required. Such objects are normally not characterised by homogeneous colour, intensity, or optical flow Therefore, conventional techniques based on these low-level features cannot perform t he desired segmentation. The authors address segmentation and tracking of m oving objects and present a new video object plane (VOP) segmentation algor ithm that extracts semantically meaningful objects. A morphological motion filter detects physical objects by identifying areas that are moving differ ently from the background. A new filter criterion is introduced that measur es the deviation of the estimated local motion from the synthesised global motion. A two-dimensional binary model is derived for the object of interes t and tracked throughout the sequence by a Hausdorff object tracker. To acc ommodate for rotations and changes in shape, the model is updated every fra me by a two-stage method that accounts for rigid and non-rigid moving parts of the object. The binary model then guides the actual VOP extraction, whe reby a novel boundary post-processor ensures high boundary accuracy. Experi mental results demonstrate the performance of the proposed algorithm.