Abstract: A system and a method are disclosed for an adaptive discriminative generative model with a probabilistic interpretation. As applied to visual tracking, the discriminative generative model separates the target object from the background more accurately and efficiently than conventional methods. A computationally efficient algorithm constantly updates the discriminative model over time. The discriminative generative model adapts to accommodate dynamic appearance variations of the target and background. Experiments show that the discriminative generative model effectively tracks target objects undergoing large pose and lighting changes.

Abstract: A system and a method are disclosed for an adaptive discriminative generative model with a probabilistic interpretation. As applied to visual tracking, the discriminative generative model separates the target object from the background more accurately and efficiently than conventional methods. A computationally efficient algorithm constantly updates the discriminative model over time. The discriminative generative model adapts to accommodate dynamic appearance variations of the target and background. Experiments show that the discriminative generative model effectively tracks target objects undergoing large pose and lighting changes.

Abstract: Visual tracking over a sequence of images is formulated by defining an object class and one or more background classes. The most discriminant features available in the images are then used to select a portion of each image as belonging to the object class. Fisher's linear discriminant method is used to project high-dimensional image data onto a lower-dimensional space, e.g., a line, and perform classification in the lower-dimensional space. The projection function is incrementally updated.

Abstract: A system and a method are disclosed for adaptive probabilistic tracking of an object within a motion video. The method utilizes a time-varying Eigenbasis and dynamic, observation and inference models. The Eigenbasis serves as a model of the target object. The dynamic model represents the motion of the object and defines possible locations of the target based upon previous locations. The observation model provides a measure of the distance of an observation of the object relative to the current Eigenbasis. The inference model predicts the most likely location of the object based upon past and present observations. The method is effective with or without training samples. A computer-based system provides a means for implementing the method. The effectiveness of the system and method are demonstrated through simulation.