Pedestrian detection based on sparse coding and transfer learning

[1]  Sheng Tang,et al.  Fast Pedestrian Detection Based on Sliding Window Filtering , 2012, PCM.

[2]  Meng Wang,et al.  Transferring a generic pedestrian detector towards specific scenes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Guillermo Sapiro,et al.  See all by looking at a few: Sparse modeling for finding representative objects , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Haibin Ling,et al.  Robust Visual Tracking and Vehicle Classification via Sparse Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Larry S. Davis,et al.  AVSS 2011 demo session: A large-scale benchmark dataset for event recognition in surveillance video , 2011, AVSS.

[7]  Meng Wang,et al.  Automatic adaptation of a generic pedestrian detector to a specific traffic scene , 2011, CVPR 2011.

[8]  Marc Van Droogenbroeck,et al.  ViBe: A Universal Background Subtraction Algorithm for Video Sequences , 2011, IEEE Transactions on Image Processing.

[9]  Qingming Huang,et al.  Transferring Boosted Detectors Towards Viewpoint and Scene Adaptiveness , 2011, IEEE Transactions on Image Processing.

[10]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[11]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[13]  Shuicheng Yan,et al.  Learning With $\ell ^{1}$-Graph for Image Analysis , 2010, IEEE Transactions on Image Processing.

[14]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[15]  H. Grabner,et al.  Classifier grids for robust adaptive object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  B. Schiele,et al.  Pedestrian detection: A benchmark , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[21]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[22]  Qiang Yang,et al.  Boosting for transfer learning , 2007, ICML '07.

[23]  Ramakant Nevatia,et al.  Improving Part based Object Detection by Unsupervised, Online Boosting , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Dariu Gavrila,et al.  An Experimental Study on Pedestrian Classification , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Frédéric Jurie,et al.  Creating efficient codebooks for visual recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[26]  Ramakant Nevatia,et al.  Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[27]  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).

[28]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[29]  Martial Hebert,et al.  Semi-Supervised Self-Training of Object Detection Models , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[30]  Thomas G. Dietterich,et al.  Improving SVM accuracy by training on auxiliary data sources , 2004, ICML.

[31]  Vinod Nair,et al.  An unsupervised, online learning framework for moving object detection , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[32]  Cordelia Schmid,et al.  Human Detection Based on a Probabilistic Assembly of Robust Part Detectors , 2004, ECCV.

[33]  Paul A. Viola,et al.  Unsupervised improvement of visual detectors using cotraining , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[34]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, International Journal of Computer Vision.

[35]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[36]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[37]  Tomaso A. Poggio,et al.  Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[38]  Charless C. Fowlkes,et al.  Do We Need More Training Data or Better Models for Object Detection? , 2012, BMVC.

[39]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[40]  Ulf Assarsson,et al.  A Benchmark for , 2001 .