We describe a state-space tracking approach based on a Conditional Random Field (CRF) model, where the observation potentials are learned from data. We find functions that embed both state and observation into a space where similarity corresponds to L1 distance, and define an observation potential based on distance in this space. This potential is extremely fast to compute and in conjunction with a grid-filtering framework can be used to reduce a continuous state estimation problem to a discrete one. We show how a state temporal prior in the grid-filter can be computed in a manner similar to a sparse HMM, resulting in real-time system performance. The resulting system is used for human pose tracking in video sequences.

Objects can exhibit different dynamics at different scales, and this is often exploited by visual tracking algorithms. A local dynamic model is typically used to extract image features that are then used as input to a system for tracking the entire object using a global dynamic model. Approximate local dynamics may be brittle - point trackers drift due to image noise and adaptive background models adapt to foreground objects that become stationary - but constraints from the global model can make them more robust. We propose a probabilistic framework for incorporating global dynamics knowledge into the local feature extraction processes. A global tracking algorithm can be formulated as a generative model and used to predict feature values that are incorporated into an observation process of the feature extractor. We combine such models in a multichain graphical model framework. We show the utility of our framework for improving feature tracking and thus shape and motion estimates in a batch factorization algorithm. We also propose an approximate filtering algorithm appropriate for online applications, and demonstrate its application to background subtraction.

Classic methods for Bayesian inference effectively constrain search to lie within regions of significant probability of the temporal prior. This is efficient with an accurate dynamics model, but otherwise is prone to ignore significant peaks in the true posterior. A more accurate posterior estimate can be obtained by explicitly finding modes of the likelihood function and combining them with a weak temporal prior. In our approach, modes are found using efficient example-based matching followed by local refinement to find peaks and estimate peak bandwidth. By reweighting these peaks according to the temporal prior we obtain an estimate of the full posterior model. We show comparative results on real and synthetic images in a high degree of freedom articulated tracking task.

Accurate representation of articulated motion is a challenging problem for machine perception. Several successful tracking algorithms have been developed that model human body as an articulated tree. We pro- pose a learning-based method for creating such articulated models from observations of multiple rigid motions. This paper is concerned with recovering topology of the articulated model, when the rigid motion of constituent segments is known. Our approach is based on finding the Maximum Likelihood tree shaped factorization of the joint probability density function (PDF) of rigid segment motions. The topology of graph- ical model formed from this factorization corresponds to topology of the underlying articulated body. We demonstrate the performance of our al- gorithm on both synthetic and real motion capture data.

A system is proposed that combines textual and visual statistics in a single index vector for content-based search of a WWW image database. Textual statistics are captured in vector form using latent semantic indexing based on text in the containing HTML document. Visual statistics are captured in vector form using color and orientation histograms. By using an integrated approach, it becomes possible to take advantage of possible statistical couplings between the content of the document (latent semantic content) and the contents of images (visual statistics). The combined approach allows improved performance in conducting content-based search. Search performance experiments are reported for a database containing 350,000 images collected from the WWW.