A hybrid framework combining background subtraction and deep neural networks for rapid person detection
暂无分享,去创建一个
[1] Armin B. Cremers,et al. Informed Haar-Like Features Improve Pedestrian Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[2] Thierry Bouwmans,et al. Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey , 2011 .
[3] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Jianguo Zhang,et al. The PASCAL Visual Object Classes Challenge , 2006 .
[5] Larry S. Davis,et al. Non-parametric Model for Background Subtraction , 2000, ECCV.
[6] W. Eric L. Grimson,et al. Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).
[7] Ezzeddine Zagrouba,et al. Abnormal behavior recognition for intelligent video surveillance systems: A review , 2018, Expert Syst. Appl..
[8] Lucia Maddalena,et al. A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.
[9] Zhiming Luo,et al. Interactive deep learning method for segmenting moving objects , 2017, Pattern Recognit. Lett..
[10] Tieniu Tan,et al. A real-time object detecting and tracking system for outdoor night surveillance , 2008, Pattern Recognit..
[11] Antoine Vacavant,et al. A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos , 2014, Comput. Vis. Image Underst..
[12] Xiaogang Wang,et al. Joint Deep Learning for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.
[13] Nigel J. B. McFarlane,et al. Segmentation and tracking of piglets in images , 1995, Machine Vision and Applications.
[14] Thierry Bouwmans,et al. Robust PCA via Principal Component Pursuit: A review for a comparative evaluation in video surveillance , 2014, Comput. Vis. Image Underst..
[15] Robin Singh Sidhu,et al. Smart surveillance system for detecting interpersonal crime , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).
[16] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Baharak Shakeri Aski,et al. Intelligent video surveillance for monitoring fall detection of elderly in home environments , 2008, 2008 11th International Conference on Computer and Information Technology.
[19] Alan F. Smeaton,et al. Background Modelling in Infrared and Visible Spectrum Video for People Tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.
[20] Xiaogang Wang,et al. Deep Learning Strong Parts for Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[21] Thierry Bouwmans,et al. Foreground Detection Using the Choquet Integral , 2008, 2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services.
[22] Borko Furht,et al. Neural Network Approach to Background Modeling for Video Object Segmentation , 2007, IEEE Transactions on Neural Networks.
[23] Subhransu Maji,et al. Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[24] Shuicheng Yan,et al. An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[25] Hongxun zhang,et al. Fusing Color and Texture Features for Background Model , 2006, FSKD.
[26] Mark E Hallenbeck,et al. Extracting Roadway Background Image , 2006 .
[27] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Thierry Bouwmans,et al. Traditional and recent approaches in background modeling for foreground detection: An overview , 2014, Comput. Sci. Rev..
[29] Sergio A. Velastin,et al. Intelligent distributed surveillance systems: a review , 2005 .
[30] Lucia Maddalena,et al. A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection , 2010, Neural Computing and Applications.
[31] Fuchun Sun,et al. HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Pietro Perona,et al. Integral Channel Features , 2009, BMVC.
[33] Pietro Perona,et al. Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] Chih-Yang Lin,et al. Three-Pronged Compensation and Hysteresis Thresholding for Moving Object Detection in Real-Time Video Surveillance , 2017, IEEE Transactions on Industrial Electronics.
[35] Piotr Dollár,et al. Crosstalk Cascades for Frame-Rate Pedestrian Detection , 2012, ECCV.
[36] Jan-Olof Eklundh,et al. Statistical background subtraction for a mobile observer , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[37] Dinesh Manocha,et al. MixedPeds: Pedestrian Detection in Unannotated Videos Using Synthetically Generated Human-Agents for Training , 2018, AAAI.
[38] Ferdinand van der Heijden,et al. Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..
[39] Yann LeCun,et al. Pedestrian Detection with Unsupervised Multi-stage Feature Learning , 2012, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[40] Jitendra Malik,et al. Semantic segmentation using regions and parts , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[41] Jitendra Malik,et al. Actions and Attributes from Wholes and Parts , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[42] Kentaro Toyama,et al. Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[43] Xiaogang Wang,et al. Switchable Deep Network for Pedestrian Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[44] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[45] Duan-Yu Chen,et al. Motion-based unusual event detection in human crowds , 2011, J. Vis. Commun. Image Represent..
[46] Xiaogang Wang,et al. Object Detection from Video Tubelets with Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Fan Yang,et al. Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Bing Li,et al. Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation , 2015, International Journal of Computer Vision.
[49] Chong-Min Kyung,et al. A Low-Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems , 2017, IEEE Transactions on Circuits and Systems for Video Technology.
[50] Xiaogang Wang,et al. Pedestrian Parsing via Deep Decompositional Network , 2013, 2013 IEEE International Conference on Computer Vision.
[51] 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).
[52] Peter H. N. de With,et al. Automatic video-based human motion analyzer for consumer surveillance system , 2009, IEEE Transactions on Consumer Electronics.
[53] Ali Farhadi,et al. YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Xiaogang Wang,et al. Pedestrian detection aided by deep learning semantic tasks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[55] Bernt Schiele,et al. Ten Years of Pedestrian Detection, What Have We Learned? , 2014, ECCV Workshops.
[56] S. Bianco,et al. How Far Can You Get By Combining Change Detection Algorithms? , 2015, ICIAP.
[57] Luis Miguel Bergasa,et al. Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls , 2015, Expert Syst. Appl..
[58] P. KaewTrakulPong,et al. An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .
[59] Daniela Moctezuma,et al. HoGG: Gabor and HoG-based human detection for surveillance in non-controlled environments , 2013, Neurocomputing.
[60] Thierry Bouwmans,et al. Background Subtraction For Visual Surveillance: A Fuzzy Approach , 2012 .
[61] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.