Person detection is a challenging task in industrial environments which typically feature rapidly changing conditions of illumination and the presence of occluding objects and cluttered background. This paper proposes a series of algorithms for improving the robustness of person detection in such harsh industrial environments. Based on a state-of-the-art person detector, significant robustness and automation is achieved by introducing automatic ground plane estimation, confidence filtering, cross-camera correspondence estimation and multi-camera fusion. Detailed experiments made on an industrial dataset that captures an automotive assembly process show the stepwise improvement when combining the above mentioned techniques in a fully unsupervised manner.
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