Feature Distilled Tracking

Robust feature tracking

Tracking without Feature Detection

The tracking method presented in this paper is based on an active-contour algorithm for pose refinement. The most important innovation with respect to most active-contour methods is that no ”features” are detected at any stage: the goodness-of-fit measure for the model is a smooth function (without thresholds) of both model parameters and image grey-levels. This smoothness leads to an estimate ...

Robust Feature Tracking

This paper addresses robust feature tracking. We extend the Shi-Tomasi-Kanade scheme by introducing a technique for rejecting spurious features. We employ a simple and efficient outlier rejection rule, called X84, and prove that its theoretical assumptions are satisfied in the feature tracking scenario. Experiments with real and synthetic images confirm that our algorithm makes good features to...

Robust Facial Feature Tracking

We present a robust technique for tracking a set of pre-determined points on a human face. To achieve robustness, the Kanade-Lucas-Tomasi point tracker is extended and specialised to work on facial features by embedding knowledge about the configuration and visual characteristics of the face. The resulting tracker is designed to recover from the loss of points caused by tracking drift or tempor...

Unscented feature tracking

Accurate feature tracking is the foundation of many high level tasks in computer vision, such as 3D reconstruction and motion analysis. Although there are many feature tracking algorithms, most of them do not maintain information about the error of the data being tracked. Also, due to the difficulty and spatial locality of the problem, existing methods can generate grossly incorrect corresponde...