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- W2054386722 abstract "In this paper, we propose a novel general framework for target detection and tracking in infrared image sequences. Anintegrated tracking system is described by this framework based on multiple models learning online. The relationsamong each component of the tracking system are expressed distinctly. Furthermore, we emphasize that the maincomponents of the tracking system shouldn't be invariable. On the contrary, they should update dynamically. Anintegrated tracking system is composed of six modules. The target appearance will change as the target object movesfrom one place to another. So the object description also needs update dynamically in the tracking framework. At thecore of many approaches for object tracking is the metric or similarity measure used to determine the distance betweenthe target template and candidates. In the proposed tracking framework, the distance measure is learnt online and updatedynamically by the ensemble learning algorithm. Approaches on estimation of object tracking can be divided into twogroups: deterministic approaches and stochastic approaches. In our unified framework, the estimation approach is notfixed, but adaptive. The observation model, motion model and number of particles can adapt to the changes of theforeground and background. Our extensive experiments show that the presented algorithm performs robustly in a largevariety of infrared image sequences. The approach proposed in this paper has the potential to solve other sensor fusionproblems." @default.
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- W2054386722 date "2009-07-03" @default.
- W2054386722 modified "2023-09-26" @default.
- W2054386722 title "A new unified framework for object detection and tracking in infrared imagery" @default.
- W2054386722 doi "https://doi.org/10.1117/12.829883" @default.
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