Tracking 3D Objects using Flexible Models

This article proposes a flexible tracker which can estimate motions and deformations of 3D objects in images by considering their appearances as nonrigid surfaces. In this approach, a flexible model is built by matching local features (key-points) over training sequences and by learning the deformations of a spline based model. A statistical model captures the variations of object appearance caused by 3D pose variations. Visual tracking is then possible, for each new frame, by matching the local features of the model with the image features, according to their local appearances and the constraints provided by the flexible model. This approach is demonstrated on real-world image sequences.