• Author / Creator
    Zhang, Xi
  • Visual tracking serves an important role in a wide variety of applications like video surveillance, robotic manipulation and augmented reality. The goal of tracking in the last two cases here is to efficiently and accurately locate the object in each frame of an image sequence/stream, with the target selected in the first frame. Various visual tracking algorithms have been proposed in literature recently, but many of them fall in the category of long-term object tracking algorithms that are more focused on the tracking robustness and are not accurate enough for such applications. On the other hand, registration based tracking algorithms can achieve greater accuracy in tracking high degree-of-freedom (DOF) object motion, but are likely to fail when fast object motion or noise in the image space is present. In this thesis, we focus on improving the robustness of registration based tracking algorithms towards fast motion and noise, while retaining good accuracy and efficiency. Concretely, we propose a novel tracking algorithm called RKLT that takes advantage of both 2D KLT trackers and the RANSAC algorithm for robust 8 DOF inter-frame target motion estimation. Inlier pixels selected by RANSAC are used to perform global registration using the efficient Inverse Compositional (IC) tracker to avoid tracking drift. In addition, we also explore the different parameterizations on the state space model of a registration based tracker which characterizes the object state in 2D image space during tracking. In particular, we show how the corner based parameterization can be applied to the 8 DOF tracker using efficient second-order minimization (ESM). The impact of different parameterizations on the performance of IC and ESM trackers is also investigated in the experiments. Finally, we introduce a new tracking dataset, Tracking for Manipulation Tasks (TMT) dataset with over 100 image sequences. New evaluation methods are also designed for better evaluation of high DOF trackers with greater accuracy. A tracking testbed is also provided for more convenient comparison among different tracking algorithms. In the experiments, the proposed RKLT algorithm performs better than three other registration based trackers, especially in the faster sequences of TMT dataset. In the public Metaio benchmark too, RKLT achieves better results than the ESM tracker which is considered the state-of-the-art.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Jagersand, Martin (Computing Science)
  • Examining committee members and their departments
    • Zhang, Hong (Computing Science)
    • Ray, Nilanjan (Computing Science)