We present a method to determine the precise shape of a dynamic object from video. This problem is fundamental to computer vision, and has a number of applications, for example, 3D video/cinema post-production, activity recognition and augmented reality. Current tracking algorithms that determine precise shape can be roughly divided into two categories: 1) Global statistics partitioning methods, where the shape of the object is determined by discriminating global image statistics, and 2) Joint shape and appearance matching methods, where a template of the object from the previous frame is matched to the next image. The former is limited in cases of complex object appearance and cluttered background, where global statistics cannot distinguish between the object and background. The latter is able to cope with complex appearance and a cluttered background, but is limited in cases of camera viewpoint change and object articulation, which induce self-occlusions and self-disocclusions of the object of interest. The purpose of this thesis is to model self-occlusion/disocclusion phenomena in a joint shape and appearance tracking framework. We derive a non-linear dynamic model of the object shape and appearance taking into account occlusion phenomena, which is then used to infer self-occlusions/disocclusions, shape and appearance of the object in a variational optimization framework. To ensure robustness to other unmodeled phenomena that are present in real-video sequences, the Kalman filter is used for appearance updating. Experiments show that our method, which incorporates the modeling of self-occlusion/disocclusion, increases the accuracy of shape estimation in situations of viewpoint change and articulation, and out-performs current state-of-the-art methods for shape tracking.