Joint components based pedestrian detection in crowded scenes using extended feature descriptors

[1]  Van-Dung Hoang,et al.  Tracking Failure Detection using Time Reverse Distance Error for Human Tracking , 2015, IEA/AIE.

[2]  Byung Ryong Lee,et al.  An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm , 2015, Vietnam Journal of Computer Science.

[3]  Bernt Schiele,et al.  Detection and Tracking of Occluded People , 2014, International Journal of Computer Vision.

[4]  Kang-Hyun Jo,et al.  Hybrid cascade boosting machine using variant scale blocks based HOG features for pedestrian detection , 2014, Neurocomputing.

[5]  Subhransu Maji,et al.  Efficient Classification for Additive Kernel SVMs , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Van-Dung Hoang,et al.  Pedestrian detection approach based on modified Haar-like features and AdaBoost , 2012, 2012 12th International Conference on Control, Automation and Systems.

[7]  Afshin Dehghan,et al.  Part-based multiple-person tracking with partial occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Jong-Soo Lee,et al.  Driver’s eye blinking detection using novel color and texture segmentation algorithms , 2012 .

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

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

[12]  Xiaofeng Wang,et al.  An efficient local Chan-Vese model for image segmentation , 2010, Pattern Recognit..

[13]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Larry S. Davis,et al.  Robust Human Detection under Occlusion by Integrating Face and Person Detectors , 2009, ICB.

[15]  De-Shuang Huang,et al.  Locally linear discriminant embedding: An efficient method for face recognition , 2008, Pattern Recognit..

[16]  De-Shuang Huang,et al.  A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks , 2008, IEEE Transactions on Neural Networks.

[17]  Chao Wang,et al.  Feature extraction using constrained maximum variance mapping , 2008, Pattern Recognit..

[18]  Jenn-Jier James Lien,et al.  AdaBoost Learning for Human Detection Based on Histograms of Oriented Gradients , 2007, ACCV.

[19]  De-Shuang Huang,et al.  A novel full structure optimization algorithm for radial basis probabilistic neural networks , 2006, Neurocomputing.

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

[21]  Jian Huang,et al.  Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  N. Dalal,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  De-Shuang Huang,et al.  Human face recognition based on multi-features using neural networks committee , 2004, Pattern Recognit. Lett..

[24]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[25]  J. Dickmann,et al.  System for object detection for vehicles , 2003 .

[26]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[27]  Tong Zhang An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods , 2001, AI Mag..

[28]  D.-S. Huang,et al.  Radial Basis Probabilistic Neural Networks: Model and Application , 1999, Int. J. Pattern Recognit. Artif. Intell..

[29]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[30]  Xiaofeng Wang,et al.  Shape recognition based on neural networks trained by differential evolution algorithm , 2007, Neurocomputing.

[31]  C. Papageorgiou,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.