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

  • Shape based appearance model
  • Steam detection
  • Joint detection and tracking
  • Adaptive multi-motion model

• Graduation date

  Spring 2012

• 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.