A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

Automated evaluation of parapapillary choroidal microvasculature in crowded optic discs:a controlled,optical coherence tomography angiography study

AIM:To compare superficial and deep vascular properties of optic discs between crowded discs and controls using optical coherence tomography angiography(OCT-A).METHODS:Thirty patients with crowded discs,and 47 control subjects were enrolled in the study.One eye of each individual was included and OCT-A scans of optic discs were obtained in a 4.5×4.5 mm^(2) rectangular area.Radial peripapillary capillary(RPC)density,peripapillary retinal nerve fiber layer(pRNFL)thickness,cup volume,rim area,disc area,cup-to-disc(c/d)area ratio,and vertical c/d ratio were obtained automatically using device software.Automated parapapillary choroidal microvasculature(PPCMv)density was calculated using MATLAB software.When the vertical c/d ratio of the optic disc was absent or small cup,it was considered as a crowded disc.RESULTS:The mean signal strength index of OCT-A images was similar between the crowded discs and control eyes(P=0.740).There was no difference in pRNFL between the two groups(P=0.102).There were no differences in RPC density in whole image(P=0.826)and peripapillary region(P=0.923),but inside disc RPC density was higher in crowded optic discs(P=0.003).The PPCMv density in the inner-hemisuperior region was also lower in crowded discs(P=0.026).The pRNFL thickness was positively correlated with peripapillary RPC density(r=0.498,P<0.001).The inside disc RPC density was negatively correlated with c/d area ratio(r=-0.341,P=0.002).CONCLUSION:The higher inside disc RPC density and lower inner-hemisuperior PPCMv density are found in eyes with crowded optic discs.

Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.

CCM-FL:Covert communication mechanisms for federated learning in crowd sensing IoT

The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

Simulation of crowd evacuation under attack considering emotion spreading

In recent years,attacks against crowded places such as campuses and theaters have had a frequent and negative impact on the security and stability of society.In such an event,the crowd will be subjected to high psychological stress and their emotions will rapidly spread to others.This paper establishes the attack-escape evacuation simulation model(AEES-SFM),based on the social force model,to consider emotion spreading under attack.In this model,(1)the attack-escape driving force is considered for the interaction between an attacker and evacuees and(2)emotion spreading among the evacuees is considered to modify the value of the psychological force.To validate the simulation,several experiments were carried out at a university in China.Comparing the simulation and experimental results,it is found that the simulation results are similar to the experimental results when considering emotion spreading.Therefore,the AEES-SFM is proved to be effective.By comparing the results of the evacuation simulation without emotion spreading,the emotion spreading model reduces the evacuation time and the number of casualties by about 30%,which is closer to the real experimental results.The results are still applicable in the case of a 40-person evacuation.This paper provides theoretical support and practical guidance for campus response to violent attacks.

A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

Deep Learning Based Efficient Crowd Counting System

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.

A Survey on Supervised,Unsupervised,and Semi-Supervised Approaches in Crowd Counting

Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety.Crowd counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and methodologies.These approaches vary in terms of supervision techniques,network architectures,and model complexity.Currently,most crowd counting methods rely on fully supervised learning,which has proven to be effective.However,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often scarce.As a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical applications.This paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision paradigms.We summarize and critically analyze the key methods in these two categories,highlighting their strengths and limitations.Furthermore,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark datasets.We believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.

Comparative analysis of manual and programmed annotations for crowd assessment and classification using artificial intelligence

Funding agencies play a pivotal role in bolstering research endeavors by allocating financial resources for data collection and analysis.However,the lack of detailed information regarding the methods employed for data gathering and analysis can obstruct the replication and utilization of the results,ultimately affecting the study’s transparency and integrity.The task of manually annotating extensive datasets demands considerable labor and financial investment,especially when it entails engaging specialized individuals.In our crowd counting study,we employed the web-based annotation tool SuperAnnotate to streamline the human annotation process for a dataset comprising 3,000 images.By integrating automated annotation tools,we realized substantial time efficiencies,as demonstrated by the remarkable achievement of 858,958 annotations.This underscores the significant contribution of such technologies to the efficiency of the annotation process.

GLCrowd:基于全局-局部注意力的弱监督密集场景人群计数模型

针对人群计数在密集场景下存在背景复杂、尺度变化大等问题,提出了一种结合全局-局部注意力的弱监督密集场景人群计数模型——GLCrowd。首先,设计了一种结合深度卷积的局部注意力模块,通过上下文权重增强局部特征,同时结合特征权重共享获得高频局部信息。其次,利用Vision Transformer(ViT)的自注意力机制捕获低频全局信息。最后,将全局与局部注意力有效融合,并通过回归令牌来完成计数。在Shanghai Tech PartA、Shanghai Tech PartB、UCF-QNRF以及UCF_CC_50数据集上进行了模型测试,MAE分别达到了64.884、8.958、95.523、209.660,MSE分别达到了104.411、16.202、173.453、282.217。结果表明,提出的GLCrowd网络模型在密集场景下的人群计数中具有较好的性能。

考虑人员特性和场景布局的西藏高原人群疏散仿真研究

为提高突发事件下西藏高原人流聚集场所人员应急疏散能力,基于模糊规则的社会力模型,结合AnyLogic对高原人群疏散行为特性进行研究。首先研究影响西藏高原人员应急疏散的因素,根据模糊规则确定因素对人员疏散速度的影响;以八廓步行街为例建立突发事件下西藏高原人员疏散仿真模型,探究人员性别、年龄、环境熟悉程度、高原适应能力、出口宽度和出口布局6因素对疏散过程的影响;采用正交实验设计分析6因素综合作用对西藏高原人员应急疏散过程产生的影响,并提出最优组合方案。研究结果表明:6因素对西藏高原人群应急疏散过程均有不同程度影响,其中疏散人员的年龄影响最大,性别影响最小,并结合仿真分析提出相应引导策略。研究结果可为西藏高原地区居民在紧急疏散情况下的行动提供科学指导和决策支持。

基于时延的Flash Crowd控制模型

提出了一种session级别的flash crowd控制策略SGAC(session-granularity admission control),将session控制粒度和request控制粒度相结合,采用请求平均返回时延作为检测和控制的依据.对session采取一旦接受就完成的策略,在实现对服务器过载控制的同时,保护用户session的完整性,并能自动调节新session的准入速率,以提高服务器利用率.采用真实HTTP Log进行模拟,结果表明,SGAC方法能够有效控制服务器过载,保护session的完整性,提高服务器利用率,降低接入端路由器计算开销,保护有价值的交易session.

CrowdTracker:一种基于移动群智感知的目标跟踪方法

面向目标跟踪问题提出一种基于移动群智感知的解决方案CrowdTracker.不同于基于视频监控的目标跟踪方法,通过基于群智的多人协作拍照方式实现对移动目标的轨迹预测和跟踪,其优化目标为在保证准确实时地对目标进行跟踪的同时尽可能地减少用户激励的成本(假设激励与完成任务的参与者人数和参与者完成任务所移动的距离成正比).为实现该目标,提出了目标移动性预测的方法MPRE和任务分配的方法T-centric,P-centric.T-centric是以任务为中心的参与者选择方法,而P-centric是以人为中心的任务选择方法.MPRE通过分析大量的车辆历史轨迹建立城市里车辆的移动模型以预测目标下一步的位置.在预测的区域内通过T-centric或P-centric方法进行跟踪任务分配.通过一个大规模的真实数据集对移动性预测方法MPRE和2种任务分配算法进行实验评估,实验结果表明:CrowdTracker能有效地在实现目标实时跟踪的同时降低激励成本.

转发概率递减的改进Crowds系统

Crowds匿名浏览系统中可以在不影响匿名度水平的前提下,通过递减转发概率减小重路由路径长度,提高系统性能。提出利用路径长度期望值递减规律确定转发概率递减比例系数的方法,仿真实验表明,新方案可以在保持原有匿名度的基础上,有效减小重路由路径长度,提高Crowds系统的通信性能。

群体机械道具表演行为建模与仿真研究

在舞台表演中,机械道具数量逐渐增多,给其控制和设计带来了巨大挑战,每次创意修改均需重新彩排,导致了效率低下和对创意修改的敏感性。为解决这些问题,提出了一种群体机械道具的表演行为模型,通过质心生长与三维线性插值生成空间状态,并利用梯度下降在时间维优化状态。通过构建三维仿真实验,对模型中的群体机械道具表演行为规划与优化进行分析,使用相似性评估仿真效果与真实表演效果,说明该模型在真实表演中的有效性,并将其应用于一次表演中,证明了该模型的可行性与先进性。

地铁车站站台局部区域人群密度计算方法

[目的]地铁车站站台人群密度计算方法是分析地铁车站站台人群聚集风险的关键。通常情况下,人群拥挤时内部密度分布不均匀,存在局部高密度区域,这也是地铁车站容易发生公共安全事故的重点区域,因此研究局部区域人群密度计算方法十分关键。[方法]通过分析站台客流集散规律,研究了各类型车站站台聚集人数随时间变化特征以及聚集人数的计算模型。基于乘客在站台屏蔽门候车区聚集特性,类比成拱现象,对聚集面积进行了修正,并结合站台聚集人数提出一种地铁车站站台局部区域人群密度计算方法。以某地铁车站为例,分析了一个列车运行间隔内站台聚集人数和行人密度的变化情况。[结果及结论]与传统平均人群密度计算方法相比,采用所提出方法计算的局部区域人群密度数值更大、变化幅度更明显,且对列车运能调整、进出站台客流变化更为敏感,能较好地体现站台负荷极值与高负荷状态持续时间所对应的极限情况。该方法在高峰期等大客流状态下应用具有较大意义。

Crowds系统中新的前驱攻击模型研究

对前驱攻击模型攻击成功的概率和攻击轮数的原理和机制进行了详细的分析,在此基础上,提出了新的统计前驱攻击模型,该模型在每一轮重路由路径建立完毕后,对路径上所有泄密节点的直接前驱进行统计,减少了攻击的重建路径次数.仿真实验表明,与前驱攻击相比,新的统计前驱攻击可以有效地提高攻击的成功概率.

基于多尺度注意力网络的密集人群计数

针对拥挤场景下的尺度变化导致人群计数任务中精度较低的问题,提出一种基于多尺度注意力网络(MANet)的密集人群计数模型。通过构建多列模型以捕获多尺度特征,促进尺度信息融合;使用双注意力模块获取上下文依赖关系,增强多尺度特征图的信息;采用密集连接重用多尺度特征图,生成高质量的密度图,之后对密度图积分得到计数。此外,提出一种新的损失函数,直接使用点注释图进行训练,以减少由高斯滤波生成新的密度图而带来的额外的误差。在公开人群数据集ShanghaiTech Part A/B、UCF-CC-50、UCF-QNRF上的实验结果均达到了最优,表明该网络可以有效处理拥挤场景下的目标多尺度,并且生成高质量的密度图。

一种基于Crowds的改进匿名通信系统

提出一种基于Crowds的改进匿名通信系统,通过结合mix匿名通信技术和填充流量技术,使改进的匿名通信系统能抵御两种攻击.此外,对改进的匿名系统在前驱攻击下的匿名性能进行了分析,推导出改进系统在前驱攻击下能提供更好的匿名保护.

Translation and Comments on Bulrush in the Crowd

Nida’s functional equivalence enjoys a great popularity among translation theories,which plays an indispensable role in the practices of translation.Bulrush in the Crowds is a lyric prose cloaked in melancholy atmosphere.This prose is written in simple but lively,vivid language.It is also highly readable,with flexible structures and various writing techniques.Short and condensed casual sentences are widely employed in this prose.Furthermore,it is good at using figure of speech.Thus,when translation is conducted,mood,structure,style and rhetorical devices should be taken into consideration.