Publications

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication


Year(s) from:  to 
Author:
Keywords (separated by spaces):

An Introduction to Random Forests for Multi-class Object Detection

Juergen Gall, Nima Razavi, Luc van Gool
Outdoor and Large-Scale Real-World Scene Analysis
, Ed.
2012, in press

Abstract

Object detection in large-scale real world scenes requires efficient multi-class detection approaches. Random forests have been shown to handle large training datasets and many classes for object detection efficiently. The most prominent example is the commercial application of random forests for gaming. In this chapter, we describe the general framework of random forests for multi-class object detection in images and give an overview of recent developments and implementation details that are relevant for practitioners.


Download in pdf format
@InCollection{eth_biwi_00925,
  title = {An Introduction to Random Forests for Multi-class Object Detection},
  booktitle = {Outdoor and Large-Scale Real-World Scene Analysis},
  pages = {},
  year = {2012},
  publisher = {},
  keywords = {},
  note = {in press}
}