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    Improving fusion of surveillance images in sensor networks using independent component analysis

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    Comparison of fusion methods for thermo-visual surveillance tracking

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    In this paper, we evaluate the appearance tracking performance of multiple fusion schemes that combine information from standard CCTV and thermal infrared spectrum video for the tracking of surveillance objects, such as people, faces, bicycles and vehicles. We show results on numerous real world multimodal surveillance sequences, tracking challenging objects whose appearance changes rapidly. Based on these results we can determine the most promising fusion scheme
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