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16 October 2013 Learning transmodal person detectors from single spectral training sets
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Annotating data for training a person detector is a tedious procedure. Therefore it is worthwhile to use freely available datasets. When detecting in the infrared spectrum it is not obvious that person images from the visible spectrum can be used to train a detector operable in IR. We show that it is possible to train a transmodel detector, which can be used to detect in IR as well as in the visible spectrum. Therefor we use integral channel features in combination with boosting based feature selection, in order to analyze which features are effective for generating the effect of transmodality.
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Hilke Kieritz, Wolfgang Hübner, and Michael Arens "Learning transmodal person detectors from single spectral training sets", Proc. SPIE 8901, Optics and Photonics for Counterterrorism, Crime Fighting and Defence IX; and Optical Materials and Biomaterials in Security and Defence Systems Technology X, 89010F (16 October 2013);

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