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Improved Person Detection in Industrial Environments using Multiple Self-Calibrated Cameras

R. Mörzinger, M. Thaler, S. Stalder, H. Grabner, and L. Van Gool
In Proceeding Workshop on Activity Monitoring by Multi-camera Surveillance Systems
2011

Abstract

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-ofthe- 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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@InProceedings{eth_biwi_00830,
  author = {R. M\"orzinger and M. Thaler and S. Stalder and H. Grabner and and L. Van Gool},
  title = {Improved Person Detection in Industrial Environments using Multiple Self-Calibrated Cameras},
  booktitle = {In Proceeding Workshop on Activity Monitoring by Multi-camera Surveillance Systems},
  year = {2011},
  keywords = {}
}