An introduction to random forests for multi-class object detection

Authors:
Juergen Gall;Nima Razavi;Luc Van Gool
Affiliations:
Computer Vision Laboratory, ETH Zurich, Switzerlanax Planck Institute for Intelligent Systems, Germany;Computer Vision Laboratory, ETH Zurich, Switzerland;Computer Vision Laboratory, ETH Zurich, SwitzerlanSAT/IBBT, Katholieke Universiteit Leuven, Belgium
Venue:
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Year:
2011

Citing 26
Cited 1

Shape quantization and recognition with randomized trees

Neural Computation
Random Forests

Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Random Subwindows for Robust Image Classification

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Randomized Trees for Real-Time Keypoint Recognition

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service

ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
Robust Object Detection with Interleaved Categorization and Segmentation

International Journal of Computer Vision
Speeded-Up Robust Features (SURF)

Computer Vision and Image Understanding
The Pascal Visual Object Classes (VOC) Challenge

International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Backprojection revisited: scalable multi-view object detection and similarity metrics for detections

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Depth-encoded hough voting for joint object detection and shape recovery

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Regression forests for efficient anatomy detection and localization in CT studies

MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Fast PRISM: Branch and Bound Hough Transform for Object Class Detection

International Journal of Computer Vision
On oblique random forests

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Real time head pose estimation from consumer depth cameras

DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Hough Forests for Object Detection, Tracking, and Action Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting highly articulated video objects with weak-prior random forests

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Scalable multi-class object detection

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real time head pose estimation with random regression forests

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Improving classifiers with unlabeled weakly-related videos

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real-time human pose recognition in parts from single depth images

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Pedestrian Detection: An Evaluation of the State of the Art

IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough-based tracking of non-rigid objects

ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Efficient regression of general-activity human poses from depth images

ICCV '11 Proceedings of the 2011 International Conference on Computer Vision

Web-based enrichment of bike sensor data for automatic geo-annotation

Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

Quantified Score

Hi-index	0.00

Visualization

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 [37]. In this paper, 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.