Local Decorrelation For Improved Pedestrian Detection
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[1] W. D. Ray,et al. Further decomposition of the Karhunen-Loève series representation of a stationary random process , 1970, IEEE Trans. Inf. Theory.
[2] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[3] P. Young,et al. Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.
[4] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Simon Kasif,et al. A System for Induction of Oblique Decision Trees , 1994, J. Artif. Intell. Res..
[6] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[7] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[8] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[9] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[10] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.
[11] Jonathan Brandt,et al. Robust object detection via soft cascade , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[12] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[13] Juan José Rodríguez Diez,et al. Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[15] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[16] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Pietro Perona,et al. Integral Channel Features , 2009, BMVC.
[18] Charless C. Fowlkes,et al. Multiresolution Models for Object Detection , 2010, ECCV.
[19] Ullrich Köthe,et al. On Oblique Random Forests , 2011, ECML/PKDD.
[20] Pietro Perona,et al. Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] Luc Van Gool,et al. Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[22] Piotr Dollár,et al. Crosstalk Cascades for Frame-Rate Pedestrian Detection , 2012, ECCV.
[23] Jitendra Malik,et al. Discriminative Decorrelation for Clustering and Classification , 2012, ECCV.
[24] Luc Van Gool,et al. Handling Occlusions with Franken-Classifiers , 2013, 2013 IEEE International Conference on Computer Vision.
[25] Deva Ramanan,et al. Exploring Weak Stabilization for Motion Feature Extraction , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[26] Anton van den Hengel,et al. Training Effective Node Classifiers for Cascade Classification , 2013, International Journal of Computer Vision.
[27] Xiaogang Wang,et al. Multi-stage Contextual Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[28] Luc Van Gool,et al. Seeking the Strongest Rigid Detector , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[29] Shengcai Liao,et al. Robust Multi-resolution Pedestrian Detection in Traffic Scenes , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[30] Joseph J. Lim,et al. Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[31] David Vázquez,et al. Random Forests of Local Experts for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[32] Xiaogang Wang,et al. Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[33] Yann LeCun,et al. Pedestrian Detection with Unsupervised Multi-stage Feature Learning , 2012, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[34] Dan Levi,et al. Fast Multiple-Part Based Object Detection Using KD-Ferns , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[35] Luc Van Gool,et al. Traffic sign recognition — How far are we from the solution? , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[36] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[37] R. Fergus,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[38] Pietro Perona,et al. Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.