Alternating Regression Forests for Object Detection and Pose Estimation

  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},
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
Highly Cited
This paper has 46 citations. REVIEW CITATIONS
28 Extracted Citations
20 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 28 extracted citations

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 20 references

Similar Papers

Loading similar papers…