Simultaneously Execution of Target Detection, Posture Estimation, and Tracking of 3D Objects by Parametric Eigenspace Method Embedded Particle Filter

In this paper, an appearance based image processing method is proposed for target detection, posture estimation, and tracking of 3D objects by Parametric Eigenspace Method embedded particle filter. In this method, the computational cost of Parametric Eigenspace Method and the tracking error can be greatly reduced by using the outputs of previous time and the preliminary knowledge of dynamics of movement of targets for a particle filter. Namely, since the particles of the particle filter can be generated in the direction where the object will be moved by using the posture estimated by Parametric Eigenspace Method for the prediction model, the accuracy of the state estimation of the object can be improved without increasing the number of particles. Therefore, the proposed method uses output result of Particle Filters and Parametric Eigenspace Method recursively and mutually for fast simultaneous execution of detection, posture estimation, and tracking of the targets. Furthermore, we demonstrate validity of our approach by several experiments.