Results 251 to 260 of about 4,521,340 (293)

Dense Crowd Counting with Capsule Networks

2020 International Conference on Systems, Signals and Image Processing (IWSSIP), 2020
In this paper, we proposed and evaluated the adoption of a capsule network-based (CapsNet-based) model rather than the convolutional neural network-based (CNN-based) models which are predominant in crowd counting tasks. The aim is to join the task of generating a high-quality density map from a single image along with producing a more precise estimate ...
Victor Hugo Roldão Reis, Silvio Jamil Ferzoli Guimarães, Zenilton Kleber Gonçalves do Patrocínio Jr.   +2 more
openaire   +1 more source

Learning from the Crowd with Neural Network

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
In general, the first step for supervised learning from crowdsourced data is integration. To obtain training data as traditional machine learning, the ground truth for each example in the crowdsourcing dataset must be integrated with consensus algorithms.
Jingjing Li, Victor S. Sheng, Zhenyu Shu, Yanxia Cheng, Yuqin Jin, Yuan-feng Yan   +5 more
openaire   +2 more sources

Crowd modeling using social networks

2015 IEEE International Conference on Image Processing (ICIP), 2015
In this work, we propose an unsupervised approach for detecting the anomalies in a crowd scene using social network model. Using a window-based approach, scene objects are first detected and tracked, and a spatio-temporal partitioning is constructed to produce a set of spatio-temporal cuboids that capture spatial and temporal features.
Rima Chaker, Imran N. Junejo, Zaher Al Aghbari   +2 more
openaire   +1 more source

Multi-Dilation Network for Crowd Counting

Proceedings of the ACM Multimedia Asia, 2019
With the growth of urban population, crowd analysis has become an important and necessary task in the field of computer vision. The goal of crowd counting, which is a subfield of crowd analysis, is to count the number of people in an image or a zone of a picture.
Shuheng Wang, Hanli Wang, Qinyu Li
openaire   +2 more sources

Learning networks, crowds and communities

Proceedings of the 1st International Conference on Learning Analytics and Knowledge, 2011
Who we learn from, where and when is dramatically affected by the reach of the Internet. From learning for formal education to learning for pleasure, we look to the web early and often for our data and knowledge needs, but also for places and spaces where we can collaborate, contribute to, and create learning and knowledge communities.
openaire   +2 more sources

Routing few robots in a crowded network

Journal of Computer and System Sciences
In Graph Coordinated Motion Planning, we are given a graph G some of whose vertices are occupied by robots, and we are asked to route k marked robots to their destinations while avoiding collisions and without exceeding a given budget 𝓁 on the number of robot moves.
Deligkas, Argyrios, Eiben, Eduard, Ganian, Robert, Kanj, Iyad, Leko, Dominik, Ramanujan, M. S.   +5 more
openaire   +3 more sources

Crowd Counting with Spatial Normalization Network

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 2020
Crowd counting, which requires to estimate crowd density from an image, is still a challenging task in computer vision. Most of the current methods are focused on large scale variation of people and ignore the huge distribution difference of crowd. To tackle these two problems together, we propose a novel framework named Spatial Normalization Network ...
Pengcheng Xia, Dapeng Zhang
openaire   +1 more source

Small Cells Placement for Crowd Networks

2018 IEEE International Conference on Communications (ICC), 2018
The deployment of small cells is one of the technical solutions to meet the challenge of data traffic rise. It reduces the cost of radio access networks. The efficiency of this solution depends on the success of cell planning to mitigate the interference. This work aims to optimize the incremental cell planning scheme that considers a preliminary macro-
Dhifallah, Khaoula, Gourhant, Yvon, Senouci, Sidi-Mohammed, Morand, Lionel   +3 more
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Aggregated context network for crowd counting

Frontiers of Information Technology & Electronic Engineering, 2020
Crowd counting has been applied to a variety of applications such as video surveillance, traffic monitoring, assembly control, and other public safety applications. Context information, such as perspective distortion and background interference, is a crucial factor in achieving high performance for crowd counting. While traditional methods focus merely
Si-yue Yu, Jian Pu
openaire   +1 more source

Relational Attention Network for Crowd Counting

2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019
Crowd counting is receiving rapidly growing research interests due to its potential application value in numerous real-world scenarios. However, due to various challenges such as occlusion, insufficient resolution and dynamic backgrounds, crowd counting remains an unsolved problem in computer vision.
Anran Zhang, Jiayi Shen, Zehao Xiao, Fan Zhu 0001, Xiantong Zhen, Xianbin Cao 0001, Ling Shao 0001   +6 more
openaire   +1 more source