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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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Beyond bag of latent topics: spatial pyramid matching for scene category recognition

Fu-xiang LU,Jun HUANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 10, Pages 817-828 doi: 10.1631/FITEE.1500070

Abstract: We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories.means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysisdescriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse sceneprod-max rule to combine information coming from multiple sources and assigns the unknown image to the sceneExperimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art

Keywords： Scene category recognition Probabilistic latent semantic analysis Bag-of-words Adaptive boosting

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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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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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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 crowdBy combining psychological and physical forces together, a full crowd simulation framework is establishedto provide a more realistic crowd simulation.Finally, we perform parameter analysis to show the believability of our model through a series of experiments

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

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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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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 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.structure analysis model is established based on purposiveness, and is used to describe the continuityWe represent the crowd with self-driven particles using a destination-driven analysis method.is a suitable descriptor for middle-density human crowds, and that the proposed destination-driven analysis

Keywords：人群建模；智能视频监控；人群稳定性

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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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Past review, current progress, and challenges ahead on the cocktail party problem Review

Yan-min QIAN, Chao WENG, Xuan-kai CHANG, Shuai WANG, Dong YU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 40-63 doi: 10.1631/FITEE.1700814

Abstract: environment, and describe the conventional single-channel techniques such as computational auditory sceneanalysis (CASA), non-negative matrix factorization (NMF) and generative models, the conventional multi-channel

Keywords： Cocktail party problem Computational auditory scene analysis Non-negative matrix factorization Permutation

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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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Animage-based approach to the reconstruction of ancient architectures by extracting and arranging 3D spatial components

Divya Udayan J,HyungSeok KIM,Jee-In KIM

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 1, Pages 12-27 doi: 10.1631/FITEE.1400141

Abstract: The objective of this research is the rapid reconstruction of ancient buildings of historical importance using a single image. The key idea of our approach is to reduce the infinite solutions that might otherwise arise when recovering a 3D geometry from 2D photographs. The main outcome of our research shows that the proposed methodology can be used to reconstruct ancient monuments for use as proxies for digital effects in applications such as tourism, games, and entertainment, which do not require very accurate modeling. In this article, we consider the reconstruction of ancient Mughal architecture including the Taj Mahal. We propose a modeling pipeline that makes an easy reconstruction possible using a single photograph taken from a single view, without the need to create complex point clouds from multiple images or the use of laser scanners. First, an initial model is automatically reconstructed using locally fitted planar primitives along with their boundary polygons and the adjacency relation among parts of the polygons. This approach is faster and more accurate than creating a model from scratch because the initial reconstruction phase provides a set of structural information together with the adjacency relation, which makes it possible to estimate the approximate depth of the entire structural monument. Next, we use manual extrapolation and editing techniques with modeling software to assemble and adjust different 3D components of the model. Thus, this research opens up the opportunity for the present generation to experience remote sites of architectural and cultural importance through virtual worlds and real-time mobile applications. Variations of a recreated 3D monument to represent an amalgam of various cultures are targeted for future work.

Keywords： Digital reconstruction 3D virtual world 3D spatial components Vision and scene understanding

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The Index System Design of the High Building Fire Hazard Assessment

Liu Aihua,Shi Shiliang,Wu Chao

Strategic Study of CAE 2006, Volume 8, Issue 9, Pages 90-94

Abstract:

Aiming at the characteristics of the high building fire, setting the fire site scene, this paper carriesout stage partition for fire on the angle of the countermeasure for fire, conducts accident analysis

Keywords： high building fire hazard index system the fire site scene stage partition for fire