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Shape Based Joint Detection and Tracking with Adaptive Multi-motion Model and its Application in Large Lump Detection

  • Author / Creator
    Wang, Zhijie
  • This thesis is motivated by a practical real application, Large Lump Detection (LLD), for which we provide a complete automatic system to detect large lumps in the oil sands mining surveillance videos. To this end, we propose a solution built around three main research components, each of which raises a specific issue, is formulated in a general way, and is tested on both the LLD problem and other similar applications. The first issue is related to the detection of objects that undergo sudden changes in motion. We formulate this problem in a joint detection and tracking (JDT) framework using multiple motion models, where these models are predicted adaptively. The prediction exploits the correlation between motion models and object kinematic state. As a result, objects are detected more accurately when they change their motion. The second issue concerns defining an appearance model which differentiates objects from background in an effective manner. We propose a novel shape based appearance model for kernel based trackers which typically model an object with a primitive geometric shape. As a result, by employing the proposed shape based appearance model, the kernel based trackers can improve their accuracy significantly. The last issue aims to ensure an object detection which handles the steam occlusion. We propose a new steam detection method which directly feeds a discrete wavelet transformed image to an Adaboost classifier. In this way, the proposed method is not only accurate because a proper classifier is learned by Adaboost, but also computationally efficient because the feature extraction step is omitted. The complete object detection solution for the LLD problem is obtained by combining the above three techniques. The proposed steam detection method ensures that objects of interest are not occluded, and then, the improved JDT method with the shape based appearance model performs the detection. Extensive experiments and encouraging results which demonstrate the effectiveness of the proposed solution to the large lump detection problem are provided.

  • Subjects / Keywords
  • Graduation date
    2012-06
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3JD2Q
  • 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
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Zhang, Hong (Computing Science)
  • Examining committee members and their departments
    • Huang, Biao (Chemical and Materials Engineering)
    • Li, Zenian (Computer Science)
    • Jagersand, Martin (Computing Science)
    • Ray, Nilanjan (Computing Science)
    • Zhang, Hong (Computing Science)