CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Bittracker—A Bitmap Tracker for Visual Tracking under Very General Conditions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we propose a new method that addresses the problem of tracking the bitmap (silhouette) of an object in a video under very general conditions. We assume a general target, possibly non rigid, with no prior information except initialization. The target, as well as the background, may change its appearance over time and the camera may move arbitrarily. The proposed algorithm fuses different visual cues by means of a conditional random field. The target's bitmap is estimated every frame by incorporating temporal color similarity, spatial color continuity and spatial motion continuity into an energy function that is minimized via min-cut. The spatial motion continuity is incorporated in the energy function in multiple image resolutions by a novel multi-scale energy term. Experiments demonstrate the robustness of our method and its advantage over other algorithms.