We introduce BM³E, a Conditional Bayesian Mixture of Experts Markov Model, for consistent
probabilistic estimates in discriminative visual tracking. The model applies to problems of …

We present a conditional temporal probabilistic framework for reconstructing 3D human
motion in monocular video based on descriptors encoding image silhouette observations …

A new, exemplar-based, probabilistic paradigm for visual tracking is presented. Probabilistic
mechanisms are attractive because they handle fusion of information, especially temporal …

This paper extends the use of statistical learning algorithms for object localization. It has
been shown that object recognizers using kernel-SVMs can be elegantly adapted to …

In this paper, we introduce a 3-D human-body tracker capable of handling fast and complex
motions in real-time. We build upon the Monte–Carlo Bayesian framework, and propose …

Visual tracking usually requires an object appearance model that is robust to changing
illumination, pose, and other factors encountered in video. Many recent trackers utilize …

Bayesian methods for visual tracking model the likelihood of image measurements
conditioned on a tracking hypothesis. Image measurements may, for example, correspond to …

This paper presents an algorithm for modeling, tracking, and recognizing human faces in
video sequences within one integrated framework. Conventional video-based face …

We introduce MMTrack (max-margin tracker), a single-target tracker that linearly combines
constant and adaptive appearance features. We frame offline single-camera tracking as a …

The effectiveness of probabilistic tracking of objects in image sequences has been
revolutionized by the development of particle filtering. Whereas Kalman filters are restricted …