Fetching the paper…
Reading the bibliography…
Crowd counting presents enormous challenges in the form of large variation in scales within images and across the dataset.
The watershed transformation applied to image segmentation
Serge Beucher et al · 1992
Earlier work this paper cites.
Markov random field models in computer vision
Stan Z Li · 1994
Earlier work this paper cites.
Privacy preserving crowd monitoring: Counting people without people models or tracking
Antoni B Chan, Zhang-Sheng John Liang, and Nuno Vasconcelos · 2008
Earlier work this paper cites.
Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection
Min Li, Zhaoxiang Zhang, Kaiqi Huang, and Tieniu Tan · 2008
Earlier work this paper cites.
Crowd analysis: a survey
Beibei Zhan, Dorothy N Monekosso, Paolo Remagnino, Sergio A Velastin, and Li-Qun Xu · 2008
Earlier work this paper cites.
Crowd counting using multiple local features
David Ryan, Simon Denman, Clinton Fookes, and Sridha Sridharan · 2009
Earlier work this paper cites.
Slic superpixels
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, Sabine Süsstrunk, et al · 2010
Earlier work this paper cites.
Learning to count objects in images
Victor Lempitsky and Andrew Zisserman · 2010
Earlier work this paper cites.
Anomaly detection in crowded scenes
Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos · 2010
Earlier work this paper cites.
Density-aware person detection and tracking in crowds
Mikel Rodriguez, Ivan Laptev, Josef Sivic, and Jean-Yves Audibert · 2011
Earlier work this paper cites.
Feature mining for localised crowd counting
Ke Chen, Chen Change Loy, Shaogang Gong, and Tony Xiang · 2012
Earlier work this paper cites.
Multi-source multi-scale counting in extremely dense crowd images
Haroon Idrees, Imran Saleemi, Cody Seibert, and Mubarak Shah · 2013
Earlier work this paper cites.
Anomaly detection and localization in crowded scenes
Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos · 2014
Earlier work this paper cites.
Crowd tracking with dynamic evolution of group structures
Feng Zhu, Xiaogang Wang, and Nenghai Yu · 2014
Earlier work this paper cites.
Convolutional neural networks for counting fish in fisheries surveillance video
Geoffrey French, Mark Fisher, Michal Mackiewicz, and Coby Needle · 2015
Earlier work this paper cites.
Hypercolumns for object segmentation and fine-grained localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2015
Earlier work this paper cites.
Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning
Haroon Idrees, Khurram Soomro, and Mubarak Shah · 2015
Earlier work this paper cites.
Crowded scene analysis: A survey
Teng Li, Huan Chang, Meng Wang, Bingbing Ni, Richang Hong, and Shuicheng Yan · 2015
Earlier work this paper cites.
Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation
Viet-Quoc Pham, Tatsuo Kozakaya, Osamu Yamaguchi, and Ryuzo Okada · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Traffic flow from a low frame rate city camera
Evgeny Toropov, Liangyan Gui, Shanghang Zhang, Satwik Kottur, and José MF Moura · 2015
Earlier work this paper cites.
Deep people counting in extremely dense crowds
Chuan Wang, Hua Zhang, Liang Yang, Si Liu, and Xiaochun Cao · 2015
Earlier work this paper cites.
Cross-scene crowd counting via deep convolutional neural networks
Cong Zhang, Hongsheng Li, Xiaogang Wang, and Xiaokang Yang · 2015
Earlier work this paper cites.
Counting in the wild
Carlos Arteta, Victor Lempitsky, and Andrew Zisserman · 2016
Earlier work this paper cites.
Crowdnet: A deep convolutional network for dense crowd counting
Lokesh Boominathan, Srinivas SS Kruthiventi, and R Venkatesh Babu · 2016
Earlier work this paper cites.
A unified multi-scale deep convolutional neural network for fast object detection
Zhaowei Cai, Quanfu Fan, Rogerio S Feris, and Nuno Vasconcelos · 2016
Earlier work this paper cites.
Laplacian pyramid reconstruction and refinement for semantic segmentation
Golnaz Ghiasi and Charless C Fowlkes · 2016
Cited alongside, same era.
Towards perspective-free object counting with deep learning
Daniel Onoro-Rubio and Roberto J López-Sastre · 2016
Cited alongside, same era.
Learning to refine object segments
Pedro O Pinheiro, Tsung-Yi Lin, Ronan Collobert, and Piotr Dollár · 2016
Cited alongside, same era.
A multi-scale cnn for affordance segmentation in rgb images
Anirban Roy and Sinisa Todorovic · 2016
Cited alongside, same era.
Beyond skip connections: Top-down modulation for object detection
Abhinav Shrivastava, Rahul Sukthankar, Jitendra Malik, and Abhinav Gupta · 2016
Cited alongside, same era.
Learning to count with cnn boosting
Elad Walach and Lior Wolf · 2016
Scale aggregation network for accurate and efficient crowd counting
Xinkun Cao, Zhipeng Wang, Yanyun Zhao, and Fei Su · 2018
Later among the works it cites.
Reverse attention for salient object detection
Shuhan Chen, Xiuli Tan, Ben Wang, and Xuelong Hu · 2018
Later among the works it cites.
Composition loss for counting, density map estimation and localization in dense crowds
Haroon Idrees, Muhmmad Tayyab, Kishan Athrey, Dong Zhang, Somaya Al-Maadeed, Nasir Rajpoot, and Mubarak Shah · 2018
Later among the works it cites.
Scale-aware fast r-cnn for pedestrian detection
Jianan Li, Xiaodan Liang, ShengMei Shen, Tingfa Xu, Jiashi Feng, and Shuicheng Yan · 2018
Later among the works it cites.
Csrnet: Dilated convolutional neural networks for understanding the highly congested scenes
Yuhong Li, Xiaofan Zhang, and Deming Chen · 2018
Later among the works it cites.
Adcrowdnet: An attention-injective deformable convolutional network for crowd understanding
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Crowd density estimation based on rich features and random projection forest
Bolei Xu and Guoping Qiu · 2016
Cited alongside, same era.
Single-image crowd counting via multi-column convolutional neural network
Yingying Zhang, Desen Zhou, Siqin Chen, Shenghua Gao, and Yi Ma · 2016
Cited alongside, same era.
Deeply supervised salient object detection with short connections
Qibin Hou, Ming-Ming Cheng, Xiaowei Hu, Ali Borji, Zhuowen Tu, and Philip HS Torr · 2017
Cited alongside, same era.
Drone-based object counting by spatially regularized regional proposal networks
Meng-Ru Hsieh, Yen-Liang Lin, and Winston H. Hsu · 2017
Cited alongside, same era.
Finding tiny faces
Peiyun Hu and Deva Ramanan · 2017
Cited alongside, same era.
Di Kang, Zheng Ma, and Antoni B Chan · 2017
Cited alongside, same era.
Ning Liu, Yongchao Long, Changqing Zou, Qun Niu, Li Pan, and Hefeng Wu · 2018
Later among the works it cites.
Path aggregation network for instance segmentation
Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia · 2018
Later among the works it cites.
Leveraging unlabeled data for crowd counting by learning to rank
Xialei Liu, Joost van de Weijer, and Andrew D. Bagdanov · 2018
Later among the works it cites.
Iterative crowd counting
Viresh Ranjan, Hieu Le, and Minh Hoai · 2018
Later among the works it cites.
Top-down feedback for crowd counting convolutional neural network
Deepak Babu Sam and R Venkatesh Babu · 2018
Later among the works it cites.
Crowd counting via adversarial cross-scale consistency pursuit
Zan Shen, Yi Xu, Bingbing Ni, Minsi Wang, Jianguo Hu, and Xiaokang Yang · 2018
Later among the works it cites.
Crowd counting with deep negative correlation learning
Zenglin Shi, Le Zhang, Yun Liu, Xiaofeng Cao, Yangdong Ye, Ming-Ming Cheng, and Guoyan Zheng · 2018
Later among the works it cites.
Multi-scale bidirectional fcn for object skeleton extraction
Fan Yang, Xin Li, Hong Cheng, Yuxiao Guo, Leiting Chen, and Jianping Li · 2018
Later among the works it cites.
Progressive attention guided recurrent network for salient object detection
Xiaoning Zhang, Tiantian Wang, Jinqing Qi, Huchuan Lu, and Gang Wang · 2018
Later among the works it cites.
Defocus blur detection via multi-stream bottom-top-bottom fully convolutional network
Wenda Zhao, Fan Zhao, Dong Wang, and Huchuan Lu · 2018
Later among the works it cites.
Crowd counting and density estimation by trellis encoder-decoder network
Xiaolong Jiang, Zehao Xiao, Baochang Zhang, Xiantong Zhen, Xianbin Cao, David Doermann, and Ling Shao · 2019
Closest in time.
Context-aware crowd counting
Weizhe Liu, Mathieu Salzmann, and Pascal Fua · 2019
Closest in time.
Revisiting perspective information for efficient crowd counting
Miaojing Shi, Zhaohui Yang, Chao Xu, and Qijun Chen · 2019
Closest in time.
Inverse attention guided deep crowd counting network
Vishwanath Sindagi and Vishal Patel · 2019
Closest in time.
Ha-ccn: Hierarchical attention-based crowd counting network
Vishwanath A Sindagi and Vishal M Patel · 2019
Closest in time.
Residual regression with semantic prior for crowd counting
Jia Wan, Wenhan Luo, Baoyuan Wu, Antoni B Chan, and Wei Liu · 2019
Closest in time.
Learning from synthetic data for crowd counting in the wild
Qi Wang, Junyu Gao, Wei Lin, and Yuan Yuan · 2019
Closest in time.
Uncertainty guided multi-scale residual learning-using a cycle spinning cnn for single image de-raining
Rajeev Yasarla and Vishal M. Patel · 2019
Closest in time.
Wide-area crowd counting via ground-plane density maps and multi-view fusion cnns
Qi Zhang and Antoni B Chan · 2019
Closest in time.
Leveraging heterogeneous auxiliary tasks to assist crowd counting
Muming Zhao, Jian Zhang, Chongyang Zhang, and Wenjun Zhang · 2019
Closest in time.