Improved person detection in industrial environments using multiple self-calibrated cameras

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. © 2011 IEEE.Mörzinger R., Thaler M., Stalder S., Grabner H., Van Gool L., ''Improved person detection in industrial environments using multiple self-calibrated cameras'', Proceedings 3rd workshop on activity monitoring by multi-camera surveillance systems - AMMCSS 2011 - in conjunction with AVSS 2011, August 30, 2011, Klagenfurt, Austria.status: publishe