A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Methods for Finding People
International Journal of Computer Vision
Ensembling neural networks: many could be better than all
Artificial Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Robust Real-Time Face Detection
International Journal of Computer Vision
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
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A Field Model for Human Detection and Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse solutions for linear prediction problems
Sparse solutions for linear prediction problems
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
The Journal of Machine Learning Research
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Photometric Invariance for Object Detection
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection and Tracking Using a Mixture of View-Based Shape–Texture Models
IEEE Transactions on Intelligent Transportation Systems
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A new cascaded L1-norm minimization learning (CLML) method for pedestrian detection in images is proposed in this paper. The proposed CLML method, which is designed from the perspective of Vapnic's theory in the statistical learning, integrates feature selection with classifier construction via solving meaningful optimization models. The method incorporates three stages: weak classifier learning, strong classifier learning and the cascaded classifier construction. In the weak classifier learning, the L1-norm minimization learning (LML) and min-max penalty function model are presented. In the strong classifier learning, an integer programming optimization model is built, equaling the reformulation of LML in the integer space. Finally, a cascade of LML classifiers is constructed to promote detection speed. During the classifier learning and pedestrian detection, Histograms of Oriented Gradients of variable-sized blocks (v-HOG) are used as feature descriptors. Experimental results on the INRIA and SDL human datasets show that the proposed method achieves a higher performance and speed than the state-of-the-art methods.