Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving object detection with boosted histograms
Image and Vision Computing
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Online Multiperson Tracking-by-Detection from a Single, Uncalibrated Camera
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time object detection on CUDA
Journal of Real-Time Image Processing
Efficient use of geometric constraints for sliding-window object detection in video
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Pedestrian Detection: An Evaluation of the State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online multi-target tracking by large margin structured learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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Macrofeatures are mid-level features that jointly encode a set of low-level features in a neighborhood. We propose a macrofeature layout selection technique to improve localization performance in an object detection task. Our method employs line, triangle, and pyramid layouts, which are composed of several local blocks represented by the Histograms of Oriented Gradients (HOGs) features in a multi-scale feature pyramid. Such macrofeature layouts are integrated into a boosting framework for object detection, where the best layout is selected to build a weak classifier in a greedy manner at each iteration. The proposed algorithm is applied to pedestrian detection and implemented using GPU. Our pedestrian detection algorithm performs better in terms of detection and localization accuracy with great efficiency when compared to several state-of-the-art techniques in public datasets.