Machine Learning
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
Pedestrian Detection in Crowded Scenes
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
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient object category recognition using classemes
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Multi-class classification on Riemannian manifolds for video surveillance
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Detecting people using mutually consistent poselet activations
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Human posture recognition for intelligent vehicles
Journal of Real-Time Image Processing
Action recognition from a distributed representation of pose and appearance
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
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
IEEE Transactions on Image Processing
Articulated part-based model for joint object detection and pose estimation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The recent availability of large scale training sets in conjunction with accurate classifiers (e.g., SVMs) makes it possible to build large sets of "simple" object detectors and to develop new classification approaches in which dictionaries of visual features are substituted by dictionaries of object detectors. The responses of this collection of detectors can then be used as a high-level image representation. In this work, we propose to go a step further in this direction by modeling spatial relations among different detector responses. We use Random Forests in order to discriminatively select spatial relations which represent frequent co-occurrences of detector responses. We demonstrate our idea in the specific people detection framework, which is a challenging classification task due to the variability of the human body articulations and appearance, and we use the recently proposed poselets as our basic object dictionary. The use of poselets is not the only possible, actually the proposed method can be applied more in general since few assumptions are made on the basic object detector. The results obtained show sharp improvements with respect to both the original poselet-based people detection method and to other state-of-the-art approaches on two difficult benchmark datasets.