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


Other title
joint detection and tracking
adaptive multi-motion model
shape based appearance model
steam detection
Type of item
Degree grantor
University of Alberta
Author or creator
Wang, Zhijie
Supervisor and department
Zhang, Hong (Computing Science)
Examining committee member and department
Huang, Biao (Chemical and Materials Engineering)
Jagersand, Martin (Computing Science)
Zhang, Hong (Computing Science)
Li, Zenian (Computer Science)
Ray, Nilanjan (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Doctor of Philosophy
Degree level
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.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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