A Particle Filtering Framework for Joint Video Tracking and Pose Estimation

A method is introduced to track the object´s motion and estimate its pose directly from 2-D image sequences. Scale-invariant feature transform (SIFT) is used to extract corresponding feature points from image sequences. We demonstrate that pose estimation from the corresponding feature points can be formed as a solution to Sylvester´s equation. We show that the proposed approach to the solution of Sylvester´s equation is equivalent to the classical SVD method for 3D-3D pose estimation. However, whereas classical SVD cannot be used for pose estimation directly from 2-D image sequences, our method based on Sylvester´s equation provides a new approach to pose estimation. Smooth video tracking and pose estimation is finally obtained by using the solution to Sylvester´s equation within the importance sampling density of the particle filtering framework. Finally, computer simulation experiments conducted over synthetic data and real-world videos demonstrate the effectiveness of our method in both robustness and speed compared with other similar object tracking and pose estimation methods.