Moving object tracking by optimizing active models

We propose a model based tracking algorithm which can extract trajectory information of a target object by detecting and tracking a moving object from a sequence of images. We use an active model which characterizes regional and structural features of a target object such as shape, texture, color, and edgeness. Our active model can adapt itself dynamically to an image sequence so that it can track a non-rigid moving object. We applied a Kalman filter to predict motion information. The predicted motion information from the Kalman filter was used very efficiently to reduce the search space in the matching process.