Patent
Digital video encoder system, method, and non-transitory computer-readable medium for tracking object regions
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TLDR
In this paper, a video compression framework based on parametric object and background compression is proposed, where an object is detected and frames are segmented into regions corresponding to the foreground object and the background.Abstract:
A video compression framework based on parametric object and background compression is proposed. At the encoder, an object is detected and frames are segmented into regions corresponding to the foreground object and the background. The encoder generates object motion and appearance parameters. The motion or warping parameters may include at least two parameters for object translation; two parameters for object scaling in two primary axes and one object orientation parameter indicating a rotation of the object. Particle filtering may be employed to generate the object motion parameters. The proposed methodology is the formalization of the concept and usability for perceptual quality scalability layer for Region(s) of Interest. A coded video sequence format is proposed which aims at “network friendly” video representation supporting appearance and generalized motion of object(s).read more
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Patent
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References
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C ONDENSATION —Conditional Density Propagation forVisual Tracking
Michael Isard,Andrew Blake +1 more
TL;DR: The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set.
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EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
Michael J. Black,Allan D. Jepson +1 more
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Robust online appearance models for visual tracking
TL;DR: A framework for learning robust, adaptive, appearance models to be used for motion-based tracking of natural objects to provide robustness in the face of image outliers, while adapting to natural changes in appearance such as those due to facial expressions or variations in 3D pose.
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Candid covariance-free incremental principal component analysis
TL;DR: A fast incremental principal component analysis (IPCA) algorithm, called candid covariance-free IPCA (CCIPCA), used to compute the principal components of a sequence of samples incrementally without estimating the covariance matrix (so covariances-free).
Proceedings ArticleDOI
Temporal texture modeling
TL;DR: This work model image sequences of temporal textures using the spatio-temporal autoregressive model (STAR), which expresses each pixel as a linear combination of surrounding pixels lagged both in space and in time.