Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning to Parse Pictures of People
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Multiple Component Learning for Object Detection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Scene Aware Detection and Block Assignment Tracking in crowded scenes
Image and Vision Computing
Re-identification of pedestrians in crowds using dynamic time warping
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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Occlusions and articulated posesmake human detectionmuch more difficult than common more rigid object detection like face or car. In this paper, a Structural Filter (SF) approach to human detection is presented in order to deal with occlusions and articulated poses. A three-level hierarchical object structure consisting ofwords, sentences and paragraphs in analog to text grammar is proposed and correspondingly each level is associated to a kind of SF, that is, Word Structural Filter (WSF), Sentences Structural Filter (SSF) and Paragraph Structural Filter (PSF). A SF is a set of detectors which is able to infer what structures a test window possesses, and specifically WSF is composed of all detectors for words, SSF is composed of all detectors for sentences, and so as PSF. WSF works on the most basic units of an object. SSF deals with meaningful sub structures of an object. Visible parts of human in crowded scene can be head-shoulder, left-part, right-part, upper-body or whole-body, and articulated human change a lot in pose especially in doing sports. Visible parts and different poses are the appearance statuses of detected humans handled by PSF. The three levels of SFs, WSF, SSF and PSF, are integrated in an embedded structure to form a powerful classifier, named as Integrated Structural Filter (ISF). Detection experiments on pedestrian in highly crowded scenes and articulated human show the effectiveness and efficiency of our approach.