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Shadow obstacle model for realistic corner-turning behavior in crowd simulation

Gao-qi HE,Yi JIN,Qi CHEN,Zhen LIU,Wen-hui YUE,Xing-jian LU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 3, Pages 200-211 doi: 10.1631/FITEE.1500253

Abstract: a novel model known as the shadow obstacle model to generate a realistic corner-turning behavior in crowdsimulation.By combining psychological and physical forces together, a full crowd simulation framework is establishedto provide a more realistic crowd simulation.

Keywords： Corner-turning behavior Crowd simulation Safety awareness Rule-based model

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Detecting interaction/complexitywithin crowd movements using braid entropy Research Papers

Murat AKPULAT, Murat EKİNCİ

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6, Pages 849-861 doi: 10.1631/FITEE.1800313

Abstract:

The segmentation of moving and non-moving regions in an image within the field of crowd analysis isa crucial process in terms of understanding crowd behavior.The purpose of this study is to better understand crowd behavior by locally measuring the degree of interaction

Keywords： Crowd behavior Motion segmentation Motion entropy Crowd scene analysis Complexity detection Braid entropy

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A Modification of Evacuation Time Computational Model andSimulation Comparison Analyses With Olympic Stadium

Zhang Qingsong,Liu Mao,Zhao Guomin

Strategic Study of CAE 2007, Volume 9, Issue 4, Pages 64-69

Abstract: EDTM uses crowd flow theory and discrete computational methods to identify various width of egress thatflow rate is variable, while the crowd flow rate as the function of crowd density based on the empiricalrelations between density and velocity of crowd movement.egress is chosen for the validity of EDTM, and a comparison of EDTM with previous model and computer simulationindicates that both the EDTM and the simulation curves are found to give better predictions than the

Keywords： evacuation time crowd flow rate egress simulation Olympic stadium

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Research on Social Risk of the Massing Crowd in Public Venues

Li Jianfeng,Liu Mao,Sui Xiaolin

Strategic Study of CAE 2007, Volume 9, Issue 6, Pages 88-93

Abstract: development of cities becomes more quick, the accidents happened in public venues resulted form massing crowdTo use the F - N curve, it is able to analyse the social risk of crowd massing venues.

Keywords： crowd massing risk social risk F-N curve quantitative risk analysis

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A platform of digital brain using crowd power Article

Dongrong XU, Fei DAI, Yue LU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 78-90 doi: 10.1631/FITEE.1700800

Abstract: A powerful platform of digital brain is proposed using crowd wisdom for brain research, based on theUsing big data, crowd wisdom, and high performance computers may significantly enhance the capability

Keywords： Artificial intelligence Digital brain Synthesis reasoning Multi-source analogical generating Crowd wisdom

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Structure Analysis of Crowd Intelligence Systems

Yunhe Pan

Engineering 2023, Volume 25, Issue 6, Pages 17-20 doi: 10.1016/j.eng.2021.08.016

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Forget less, count better: a domain-incremental self-distillation learning benchmark for lifelong crowd Research Article

Jiaqi GAO, Jingqi LI, Hongming SHAN, Yanyun QU, James Z. WANG, Fei-Yue WANG, Junping ZHANG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2, Pages 187-202 doi: 10.1631/FITEE.2200380

Abstract: has important applications in public safety and pandemic control. A robust and practical system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain only. Off-the-shelf methods have some drawbacks when handling multiple domains: (1) the models will achieve limited performance (even drop dramatically) among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains, which is called catastrophic forgetting; (2) the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift; (3) it leads to linearly increasing storage overhead, either mixing all the data for training or simply training dozens of separate models for different domains when new ones are available. To overcome these issues, we investigate a new task in incremental domain training setting called lifelong . Its goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental domains. Specifically, we propose a self-distillation learning framework as a benchmark (forget less, count better, or FLCB) for lifelong , which helps the model leverage previous meaningful knowledge in a sustainable manner for better to mitigate the forgetting when new data arrive. A new quantitative metric, normalized Backward Transfer (nBwT), is developed to evaluate the forgetting degree of the model in the process. Extensive experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

Keywords： Crowd counting Knowledge distillation Lifelong learning

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Aggregated context network for crowd counting

Si-yue Yu, Jian Pu,51174500148@stu.ecnu.edu.cn,jianpu@fudan.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11, Pages 1535-1670 doi: 10.1631/FITEE.1900481

Abstract: 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 . While traditional methods focus merely on solving one specific factor, we aggregate sufficient context information into the network to tackle these problems simultaneously in this study. We build a fully convolutional network with two tasks, i.e., main density map estimation and auxiliary . The main task is to extract the multi-scale and spatial context information to learn the density map. The auxiliary task gives a comprehensive view of the background and foreground information, and the extracted information is finally incorporated into the main task by late fusion. We demonstrate that our network has better accuracy of estimation and higher robustness on three challenging datasets compared with state-of-the-art methods.

Keywords：人群计数；卷积神经网络；密度估计；语义分割；多任务学习

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Crowd intelligence in AI 2.0 era Review

Wei LI,Wen-jun WU,Huai-min WANG,Xue-qi CHENG,Hua-jun CHEN,Zhi-hua ZHOU,Rong DING

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1, Pages 15-43 doi: 10.1631/FITEE.1601859

Abstract: As one of the most prominent characteristics of research in AI 2.0 era, crowd intelligence has attractedIn particular, due to the rapid development of the sharing economy, crowd intelligence not only becomesIn this paper, we survey existing studies of crowd intelligence.Then, we introduce four categories of representative crowd intelligence platforms.Finally, we discuss promising future research directions of crowd intelligence.

Keywords： Crowd intelligence Artificial intelligence 2.0 Crowdsourcing Human computation

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A novel convolutional neural network method for crowd counting Research Articles

Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen,18s004055@hit.edu.cn,dixiaoguang@hit.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 8, Pages 1119-1266 doi: 10.1631/FITEE.1900282

Abstract: Crowd , in general, is a challenging task due to the large variation of head sizes in the crowds.networks, i.e., a foreground-segmentation convolutional neural network (FS-CNN) as the front end and a crowd-regression

Keywords： Crowd counting Density estimation Segmentation prior map Uniform function

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The extension of simulation —— from system simulation to domain simulation

28 Nov 2020

Keywords：仿真技术

Heading toward Artificial Intelligence 2.0

Yunhe Pan

Engineering 2016, Volume 2, Issue 4, Pages 409-413 doi: 10.1016/J.ENG.2016.04.018

Abstract:

With the popularization of the Internet, permeation of sensor networks, emergence of big data, increase in size of the information community, and interlinking and fusion of data and information throughout human society, physical space, and cyberspace, the information environment related to the current development of artificial intelligence (AI) has profoundly changed. AI faces important adjustments, and scientific foundations are confronted with new breakthroughs, as AI enters a new stage: AI 2.0. This paper briefly reviews the 60-year developmental history of AI, analyzes the external environment promoting the formation of AI 2.0 along with changes in goals, and describes both the beginning of the technology and the core idea behind AI 2.0 development. Furthermore, based on combined social demands and the information environment that exists in relation to Chinese development, suggestions on the development of AI 2.0 are given.

Keywords： Artificial intelligence 2.0 Big data Crowd intelligence Cross-media Human-machine hybrid-augmented intelligence

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Crowd modeling based on purposiveness and a destination-driven analysis method Research Articles

Ning Ding, Weimin Qi, Huihuan Qian,hhqian@cuhk.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 10, Pages 1351-1369 doi: 10.1631/FITEE.2000312

Abstract: This study focuses on the multiphase flow properties of crowd motions.Stability is a crucial forewarning factor for the crowd.To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd, a novel motionWe represent the crowd with self-driven particles using a destination-driven analysis method.crowds, and that the proposed destination-driven analysis method is capable of representing complex crowd

Keywords：人群建模；智能视频监控；人群稳定性

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Development and Application of Simulation Technology

Wang Zicai

Strategic Study of CAE 2003, Volume 5, Issue 2, Pages 40-44

Abstract:

This paper discusses the developing process of simulation technology in view of its development, maturationThen this paper introduces the application of simulation technology in the fields of national economyFinally, this paper analyzes the level and status quo of home and overseas simulation technology, and

Keywords： simulation technology system simulation hardware in loop simulation distributed interactive simulation

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Disambiguating named entitieswith deep supervised learning via crowd labels Article

Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1, Pages 97-106 doi: 10.1631/FITEE.1601835

Abstract: discriminative features in the manner of collaborative utilization of collective wisdom (via human-labeled crowdIn particular, we devise a crowd model to elicit the underlying features (crowd features) from crowdlabels that indicate a matching candidate for each mention, and then use the crowd features to fine-tuneThe learned DCNN is employed to obtain deep crowd features to enhance traditional hand-crafted featuresThe proposed method substantially benefits from the utilization of crowd knowledge (via crowd labels)

Keywords： Named entity disambiguation Crowdsourcing Deep learning