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Technical Program
Paper Detail
| Paper: | TP-L5.7 |
| Session: | Video Surveillance |
| Time: | Tuesday, October 10, 16:40 - 17:00 |
| Presentation: |
Lecture
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| Title: |
ROBUST KERNEL-BASED TRACKING USING OPTIMAL CONTROL |
| Authors: |
Wei Qu; University of Illinois at Chicago | | | | | Dan Schonfeld; University of Illinois at Chicago | | |
| Abstract: |
Although more efficient in computation compared to other tracking approaches such as particle filtering, the kernel-based tracking suffers from the well-known ``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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