Open-Contour Tracking Using a New State-Space Model and Nonrigid Motion Training
Object tracking in a video sequence is usually achieved by tracking the bounding box over the object or the object’s boundary, each of which has somewhat different applications. In this paper, we present a new open-contour tracking algorithm based on a Bayesian framework in which the contour is a part of the object’s boundary. We first propose a new state-space model for the representation of contours, which can handle the rigid and nonrigid motions of contours independently. This model enables us to focus on the nonrigid motions during the training, and the model works for challenging rigid motion scenarios. In addition, for the robust tracking of contours, we propose a measurement function that considers the contrast on object boundaries, target appearance, and temporal coherence. We applied the proposed method to two kinds of open-contours targets, and the experimental results show that the proposed method achieves superior performance to the conventional contour tracking methods. The proposed method is also compared with recent bounding box tracking methods for the object tracking purposes, and the comparison shows that the proposed method works robustly to fast motions and yields a more accurate estimate of an object’s location than the conventional bounding box tracking methods.