Rafael Mosberger, Bastian Leibe, Henrik Andreasson and Achim J. Lilienthal
Multi-band Hough Forests for Detecting Humans with Reflective Safety Clothing from Mobile Machinery
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2015.
Abstract:

We address the problem of human detection from heavy mobile machinery and robotic equipment operating at industrial working sites. Exploiting the fact that workers are typically obliged to wear high-visibility clothing with reflective markers, we propose a new recognition algorithm that specifically incorporates the highly discriminative features of the safety garments in the detection process. Termed Multi-band Hough Forest, our detector fuses the input from active near-infrared (NIR) and RGB color vision to learn a human appearance model that not only allows us to detect and localize industrial workers, but also to estimate their body orientation. We further propose an efficient pipeline for automated generation of training data with high-quality body part annotations that are used in training to increase detector performance. We report a thorough experimental evaluation on challenging image sequences from a real-world production environment, where persons appear in a variety of upright and non-upright body positions.

Keywords: Human Tracking, Industrial Safety, Reflective Vest Detection
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@INPROCEEDINGS{Mosberger_etal:ICRA:2015,
  AUTHOR = {Mosberger, Rafael and Leibe, Bastian and Andreasson, Henrik and Lilienthal, Achim J.},
  TITLE = {Multi-band Hough Forests for Detecting Humans with Reflective Safety Clothing from Mobile Machinery},
  BOOKTITLE = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
  YEAR = {2015},
}