Model-less pose tracking

Acquiring 3-D motion of a camera from image sequences is one of the key components in a wide range of applications such as human computer interaction. Given the 3-D structure, the problem of camera motion recovery can he solved using the model-based approaches, which are well-known and have good performance under a controlled environment. If prior information on the scene is not available, traditional Structure from Motion (SFM) algorithms, which simultaneously estimate the scene structure and pose information, are required. The research presented in this thesis belongs to a different category: Motion from Motion (MFM), in which the main concern is the camera position and orientation. To be more precise, MFM algorithms have the capability of estimating 3-D camera motion directly from 2-D image motion without the explicit reconstruction of the scene structure, even though the 3-D model structure is not known in prior. As keeping track of the structural information is no longer required, putting these types of algorithms into real applications is relatively easy and convenient.;The objective of this thesis is to develop a high-speed recursive approach that tackles the MFM problem. On the way to the final goal, a series of methods, each having its own strengths and characteristics, have been studied. (1) The first algorithm computes the camera pose from a monocular image sequence. The trifocal tensor is incorporated into the Extended Kalman Filter (EKF) formulation. The step of computing the 3-D models can thus be eliminated. (2) The proposed approach is then extended to the recovery of motion from a stereo image sequence. By applying the trifocal tensor to a stereo vision framework, the trifocal constraint becomes more robust and is not likely to be degenerate. In addition, the twist motion model is adopted to parameterize the 3-D motion. It does not suffer from singularities as Euler angles, and is minimal as opposed to quaternion and the direct use of rotation matrix. (3) The third method introduces the Interacting Multiple Model Probabilistic Data Association Filter (IMMPDAF) to the MFM problem. The Interacting Multiple Model (IMM) technique allows the existence of more than one dynamic system and in return leads to improved accuracy and stability even under abrupt motion changes. The Probabilistic Data Association (PDA) framework makes the automatic selection of measurement sets possible, resulting in enhanced robustness to occlusions and moving objects. As the PDA associates stereo correspondences probabilistically, the explicit establishment of stereo matches is not necessary except during initialization, and the point features present in the outer region of the stereo images can be utilized.;It is demonstrated in the experiments that the proposed algorithms are efficient, stable and accurate compared to several existing approaches. Furthermore, they have been put into applications such as mixed reality, virtual reality, robotics and super-resolution to show their performance in real situations.