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Robust Kernel-Based Tracking using Optimal Control
Wei Qu; Schonfeld, D.;
Image Processing, 2006 IEEE International Conference on
8-11 Oct. 2006
Page(s):1777
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1780
Abstract:
Although more efficient in computation compared to other tracking approaches such as particle filtering, the kernel-based tracking suffers from the "singularity" problem which makes the tracking unstable and even completely fail. In this paper, we propose a novel framework to handle this problem by enhancing the tracker's observability. In particular, we formulate object tracking as an inverse problem, thus unifying the existing kernel-based tracking approaches into a consistent theoretical framework. By exploiting the observability theory, we explicitly give the criterion for kernel design and constraint selection. Moreover, we extend the kernel-based approach by including the state dynamics and thus form a state-space model. The use of observability theory is also extended for dynamics estimation and evaluation. We rely on an optimal observer for state estimation as a solution to video tracking. The performance of the proposed approach has been demonstrated on both synthetic and real-world video data and compared to other kernel-based tracking approaches
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