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Recently, several approaches have been introduced for incorporating the information from multiple cameras to increase the robustness of tracking. This allows to handle problems of mutually occluding objects - a reasonable scenario for many tasks such as visual surveillance or sports analysis. However, these methods often ignore problems such as inaccurate geometric constraints and violated geometric assumptions, requiring complex methods to resolve the resulting errors. In this paper, we introduce a new multiple camera tracking approach that inherently avoids these problems. We build on the ideas of generalized Hough voting and extend it to the multiple camera domain. This offers the following advantages: we reduce the amount of data in voting and are robust to projection errors. Moreover, we show that using additional geometric information can help to train more specific classifiers drastically improving the tracking performance. We confirm these findings by comparing our approach to existing (multi-camera) tracking methods.