| Summary: | 碩士 === 國立交通大學 === 資訊工程系所 === 93 === Detecting and tracking moving objects has a wide variety of applications in computer vision such as computer vision, video surveillance, traffic monitoring, etc. Additionally, it provides input to higher level vision tasks. This thesis presents an approach to tracking a moving object over a sequence of images. In particular, we improve the Abdol-Reza’s model by coupling with shape prior knowledge for shape perseverance in case of ambiguity.
In the model of Abdol-Reza, tracking is achieved by evolving the contour from frame to frame by minimizing an energy functional evaluated by Bayesian theory. There are three two favorable features in this model. First, no motion field or parameters needed to be computed. Second, deformable shapes of the object are allowed and the topology of the boundary is not constrained. Third, no assumption is made on the strength of the edge gradient. However, it also suffers from the constraints imposed on a degree of dissimilarity between the object and the background. A background region similar to the object might corrupt the contour evolution. We want to overcome this drawback by coupling with a shape prior in the associated energy functional. When the object is partially involved in a similar background, the original tracking term in the functional will dominate the result in the unambiguous background part, and the prior shape will guide the object movement in the ambiguous part. If the object is entirely distinguishable from the background, the weight of the shape prior is set low and thus allowing free deformation of the object. Compared to other tracking methods embedded with shape priors, the presented approach is more flexible, retaining the advantage of suffering little constraints on the deformable shape of the tracked object in many cases.
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