Alternating Regression Forests for Object Detection and Pose Estimation

@article{Schulter2013AlternatingRF,
  title={Alternating Regression Forests for Object Detection and Pose Estimation},
  author={Samuel Schulter and Christian Leistner and Paul Wohlhart and Peter M. Roth and Horst Bischof},
  journal={2013 IEEE International Conference on Computer Vision},
  year={2013},
  pages={417-424}
}
We present Alternating Regression Forests (ARFs), a novel regression algorithm that learns a Random Forest by optimizing a global loss function over all trees. This interrelates the information of single trees during the training phase and results in more accurate predictions. ARFs can minimize any differentiable regression loss without sacrificing the appealing properties of Random Forests, like low computational complexity during both, training and testing. Inspired by recent developments for… CONTINUE READING
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