Robust object tracking plays a central role in many applications of image processing, computer vision and automatic control. In this thesis, robust object tracking under complex environments, including heavy clutters in the background, low resolution of the image sequences and non-stationary camera, has been studied. The interest of this study stems from the improvement of the performance of visual tracking using particle filtering. A Geometric Active contour-based Tracking Estimator, namely GATE, has been developed in order to tackle the problems in robust object tracking where the existence of multiple features or good object detection is not guaranteed. GATE combines particle filtering and the level set-based active contour method. The particle filtering method is able to deal with nonlinear and non-Gaussian recursive estimation problems, and the level set-based active contour method is capable of classifying state space of particle filtering under the methodology of one class classification. By integrating this classifier into the particle filtering, geometric information introduced by the shape prior and pose invariance of the tracked object in the level set-based active contour method can be utilised to prevent the particles corresponding to outlier measurements from being heavily reweighted. Hence, this procedure reshapes and refines the posterior distribution of the particle filtering. To verify the performance of GATE, the performance of the standard particle filter is compared with that of GATE. Since video sequences in different applications are usually captured by diverse devices, GATE and the standard particle filters with the identical initialisation are studied on image sequences captured by the handhold, stationary and PTZ camera, respectively. According to experimental results, even though a simple color observation model based on the Hue-Saturation-Value (HSV) color histogram is adopted, the newly developed. GATE significantly improves the performance of the particle filtering for object tracking in complex environments. Meanwhile, GATE initialises a novel approach to tackle the impoverishment problem for recursive Bayesian estimation using sampling method.
| Identifer | oai:union.ndltd.org:ADTP/258672 |
| Date | January 2009 |
| Creators | Luo, Cheng, Computer Science & Engineering, Faculty of Engineering, UNSW |
| Publisher | Publisher:University of New South Wales. Computer Science & Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://unsworks.unsw.edu.au/copyright, http://unsworks.unsw.edu.au/copyright |
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