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Robust Kernel-Based Tracking using Optimal Control

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Robust Kernel-Based Tracking using Optimal Control
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 sy...
Wei Qu, Dan Schonfeld
Added 22 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2006
Where ICIP
Authors Wei Qu, Dan Schonfeld
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