CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
暂无分享,去创建一个
[1] Shaogang Gong,et al. Crowd Counting and Profiling: Methodology and Evaluation , 2013, Modeling, Simulation and Visual Analysis of Crowds.
[2] Yao Zhao,et al. Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Yu Wang,et al. Going Deeper with Embedded FPGA Platform for Convolutional Neural Network , 2016, FPGA.
[4] Saturnino Maldonado-Bascón,et al. Extremely Overlapping Vehicle Counting , 2015, IbPRIA.
[5] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[6] Daniel Oñoro-Rubio,et al. Towards Perspective-Free Object Counting with Deep Learning , 2016, ECCV.
[7] Deming Chen,et al. High-performance video content recognition with long-term recurrent convolutional network for FPGA , 2017, 2017 27th International Conference on Field Programmable Logic and Applications (FPL).
[8] Xiaogang Wang,et al. Cross-scene crowd counting via deep convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[10] Xiaogang Wang,et al. Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset , 2016, IEEE Transactions on Multimedia.
[11] Yunchao Wei,et al. STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Graham W. Taylor,et al. Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[13] Noel E. O'Connor,et al. Shallow and Deep Convolutional Networks for Saliency Prediction , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Bingbing Ni,et al. Crowded Scene Analysis: A Survey , 2015, IEEE Transactions on Circuits and Systems for Video Technology.
[16] David A. McAllester,et al. Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Seunghoon Hong,et al. Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[18] Sergio A. Velastin,et al. Crowd analysis: a survey , 2008, Machine Vision and Applications.
[19] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[20] Nuno Vasconcelos,et al. Bayesian Poisson regression for crowd counting , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[21] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[22] Haroon Idrees,et al. Multi-source Multi-scale Counting in Extremely Dense Crowd Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[23] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[24] Luca Benini,et al. YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights , 2016, 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI).
[25] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Vishal M. Patel,et al. Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[27] Ullrich Köthe,et al. Learning to count with regression forest and structured labels , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).
[28] Di He,et al. Machine learning on FPGAs to face the IoT revolution , 2017, 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).
[29] Haizhou Ai,et al. End-to-end crowd counting via joint learning local and global count , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[30] José M. F. Moura,et al. FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[31] Shaogang Gong,et al. Cumulative Attribute Space for Age and Crowd Density Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[32] Srinivas S. Kruthiventi,et al. CrowdNet: A Deep Convolutional Network for Dense Crowd Counting , 2016, ACM Multimedia.
[33] Ryuzo Okada,et al. COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[34] Nuno Vasconcelos,et al. Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[35] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[36] Lior Wolf,et al. Learning to Count with CNN Boosting , 2016, ECCV.
[37] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[38] Shenghua Gao,et al. Single-Image Crowd Counting via Multi-Column Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Shiv Surya,et al. Switching Convolutional Neural Network for Crowd Counting , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[41] 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).
[42] Pietro Perona,et al. Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Vishal M. Patel,et al. CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting , 2017, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
[44] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.
[45] Andrew Zisserman,et al. Learning To Count Objects in Images , 2010, NIPS.
[46] Noel E. O'Connor,et al. Fully Convolutional Crowd Counting on Highly Congested Scenes , 2016, VISIGRAPP.