Results 231 to 240 of about 318,999 (276)

Adaptive weighted crowd receptive field network for crowd counting

Pattern Analysis and Applications, 2020
Crowd counting plays an important role in crowd analysis and monitoring. To this end, we propose a novel method called Adaptive Weighted Crowd Receptive Field Network (AWRFN) for crowd counting to estimate the number of people and the spatial distribution of input crowd images.
Sifan Peng, Luyang Wang, Baoqun Yin, Yun Li, Yinfeng Xia, Xiaoliang Hao   +5 more
openaire   +1 more source

Social network of the competing crowd

2014 International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC2014), 2014
In this paper, we analyze the social network among competition participants at Kaggle.com. In particular, individuals, called members, are allowed to participate in various competition in teams. Each team may have one or more members. As a result, the relationship between teams and members may be represented as a bipartite graph.
Kai Lu, Wenjun Zhou 0001, Xuehua Wang
openaire   +1 more source

The Communication Network Within the Crowd

Proceedings of the 25th International Conference on World Wide Web, 2016
Since its inception, crowdsourcing has been considered a black-box approach to solicit labor from a crowd of workers. Furthermore, the "crowd" has been viewed as a group of independent workers dispersed all over the world. Recent studies based on in-person interviews have opened up the black box and shown that the crowd is not a collection of ...
Ming Yin 0001, Mary L. Gray, Siddharth Suri, Jennifer Wortman Vaughan   +3 more
openaire   +1 more source

Feature Reaggregation Network for Crowd Counting

2020 4th International Conference on Advances in Image Processing, 2020
In this paper, we propose a novel end-to-end network named Feature Reaggregation Network (FRNet) for crowd counting, which focuses on fusing the multi-scale features in the hierarchy for generating high-quality density maps. Two level and three level feature reaggregation modules are developed between the backbone network and the next feature ...
Xiaoliang Hao, Qianqian Yang, Yinfeng Xia, Baoqun Yin, Sifan Peng   +4 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   +1 more source

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

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

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   +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   +1 more source

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