Branch&Rank for Efficient Object Detection




Ranking hypothesis sets is a powerful concept for efficient object detection. In this work, we propose a branch&rank scheme that detects objects with often less than 100 ranking operations. This efficiency enables the use of strong and also costly classifiers like non-linear SVMs with RBF-TeX kernels. We thereby relieve an inherent limitation of branch&bound methods as bounds are often not tight enough to be effective in practice. Our approach features three key components: a ranking function that operates on sets of hypotheses and a grouping of these into different tasks. Detection efficiency results from adaptively sub-dividing the object search space into decreasingly smaller sets. This is inherited from branch&bound, while the ranking function supersedes a tight bound which is often unavailable (except for rather limited function classes). The grouping makes the system effective: it separates image classification from object recognition, yet combines them in a single formulation, phrased as a structured SVM problem. A novel aspect of branch&rank is that a better ranking function is expected to decrease the number of classifier calls during detection. We use the VOC’07 dataset to demonstrate the algorithmic properties of branch&rank.

Author(s): Alain Lehmann and Peter Gehler and Luc VanGool
Journal: International Journal of Computer Vision
Year: 2013
Month: December
Publisher: Springer

Department(s): Perceiving Systems
Research Project(s): Learning to infer
Bibtex Type: Article (article)
Paper Type: Journal

Attachments: pdf


  title = {Branch\&Rank for Efficient Object Detection},
  author = {Lehmann, Alain and Gehler, Peter and VanGool, Luc},
  journal = {International Journal of Computer Vision},
  publisher = {Springer},
  month = dec,
  year = {2013},
  url = {},
  month_numeric = {12}