Crowd Counting人群计数是公共安防行业中非常需要的一种技术。对于给定的一幅图像或一段视频,通过计算机自动处理,分析出其中的人数。
Qiaosi Yi, Yunxing Liu,Aiwen Jiang, Juncheng Li, Kangfu Mei,Mingwen Wang
We proposed a Scale-Aware Crowd Counting Network that can efficiently deal with the task of crowd counting under clutter background and scale variations
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AAAI, pp.12837-12844, (2020)
Unlike multi-view multi-scale, we propose to solve the multi-view crowd counting task through 3D feature fusion with 3D scene-level density maps, instead of the 2D ground-plane ones
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ICASSP, pp.1848-1852, (2020)
We found that a mass of estimation errors in the background areas impede the performance of the existing methods
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Reddy Mahesh Kumar Krishna, Hossain Mohammad,Rochan Mrigank,Wang Yang
WACV, pp.2803-2812, (2020)
The key reason for this surge in interest is the demand of automated complex crowd scene understanding that appears in computer vision applications such as surveillance, traffic monitoring, etc
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european conference on computer vision, pp.212-229, (2020)
We show that the proposed method is able to recover a major percentage of the performance drop. Synthetic-to-real transfer settings: In this setting, the goal is to train on synthetic dataset, while adapting to real-world dataset
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european conference on computer vision, pp.747-766, (2020)
In Neural Architecture Search-Count we propose a counting-oriented NAS framework with specific search space, search strategy, and supervision method, what we use to develop our Automatic Multi-Scale Network
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Sajid Usman, Sajid Hasan, Wang Hongcheng, Wang Guanghui
IEEE Transactions on Circuits and Systems for Video Technology, pp.1-1, (2020)
This paper proposes a novel approach for crowd counting in low to high density scenarios in static images
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CVPR, pp.4705-4714, (2020)
We have presented a novel attention scaling based counting network that exploits attention masks and scaling factors to correct density estimations in regions of different density levels
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Yifan Yang,Guorong Li, Zhe Wu, Li Su,Qingming Huang,Nicu Sebe
CVPR, pp.4373-4382, (2020)
We propose a reverse perspective network to diminish the scale variations while estimating the crowd densities
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Chen Xinya, Bin Yanrui,Gao Changxin,Sang Nong,Tang Hao
Neurocomputing, (2020): 399-408
We propose Relevant Region Prediction for crowd counting, which consists of the Count Map and the Region Relation-Aware Module
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Sindagi Vishwanath A.,Yasarla Rajeev,Patel Vishal M.
IEEE transactions on pattern analysis and machine intelligence, (2020): 1-1
We introduce a new large scale unconstrained crowd counting dataset that contains "4,372" images with "1.51 million" annotations
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Ao Luo,Fan Yang, Xin Li, Dong Nie, Zhicheng Jiao,Shangchen Zhou,Hong Cheng
AAAI, pp.11693-11700, (2020)
We propose a novel method for crowd counting with a hybrid graph model
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Deepak Babu Sam, Skand Vishwanath Peri, Mukuntha Narayanan Sundararaman, Amogh Kamath, Venkatesh Babu Radhakrishnan
IEEE transactions on pattern analysis and machine intelligence, (2020): 1-1
This paper introduces a dense detection framework for crowd counting and renders the prevalent paradigm of density regression obsolete
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Haoyue Bai, S. -H. Gary Chan
We provide a comprehensive overview and comparison of three major design modules for deep learning models in crowd counting, deep neural network design, loss function, and supervisory signal
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We propose a topological constraint and a novel persistence loss based on persistent homology theory
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Mingjie Wang, Hao Cai, Xianfeng Han,Jun Zhou,Minglun Gong
We present a novel network, Scale Tree Network, which consistently addresses the challenges of drastic scale variations, density changes, and complex background
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Wei Xu, Dingkang Liang, Yixiao Zheng,Zhanyu Ma
We propose a novel Dilated-Scale-Aware Category-Attention ConvNet, which achieves multi-class object counting simultaneously only based on point-level annotations
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We introduce the first RGBT crowd counting benchmark with 2,030 pairs of RGB-thermal images and 138,389 annotated people
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This paper proposes an effective crowd localization framework, Independent Instance Map, which outputs independent instance maps to localize each head in crowd scenes
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Guangshuai Gao,Qingjie Liu, Qi Wen,Yunhong Wang
This paper proposes a novel crowd counting approach based on pyramidal scale module and global context module, dubbed Pyramidal Scale and global Context Network
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