System and process for bootstrap initialization of nonparametric color models

Abstract

A system and process for automatically learning a reliable color-based tracking system is presented. The tracking system is learned by using information produced by an initial object model in combination with an initial tracking function to probabilistically determine the configuration of one or more target objects in a temporal sequence of images, and a data acquisition function for gathering observations relating to color in each image. The observations gathered by the data acquisition function include information that is relevant to parameters desired for a final color-based object model. A learning function then uses probabilistic methods to determine conditional probabilistic relationships between the observations and probabilistic target configuration information to learn a color-based object model automatically tailored to specific target objects. The learned object model is then used in combination with the final tracking function to probabilistically locate and track specific target objects in one or more sequential images.

Description

BACKGROUND 1. Technical Field The invention is related to a system and process for automatically generating a reliable color-based tracking system, and more particularly, to a system and process for using information gathered from an initial object tracking system to automatically learn a color-based object model tailored to at least one specific target object, to create a tracking system more reliable than the initial object tracking system. 2. Related Art Most current systems for determining the presence of objects of interest in an image or scene have involved processing a temporal sequence of color or grayscale images of a scene using a tracking system. Objects are typically recognized, located and / or tracked in these systems using, for example, color-based, edge-based, shape-based, or motion-based tracking schemes to process the images. While the aforementioned tracking systems are useful, they do have limitations. For example, such object tracking systems typically use a ...

AI Technical Summary

Problems solved by technology

While the aforementioned tracking systems are useful, they do have limitations.

However, it is not generally possible to create a single high fidelity object model that ideally represents each of the many potential derivatives or views of a single object type, such as the faces of different individuals having different skin coloration, facial structure, hair type and style, etc., under any of a number of lighting conditions.

Consequently, such tracking systems are prone to error, especially where the actual parameters defining the targe...

Images