人群异常识别技术研究进展

人群行为分析是计算机视觉领域最活跃的研究方向之一. 有许多针对人群异常行为及检测的算法如人群密度估计、人群中运动检测、人群跟踪和群体行为识别. 在对目前人群异常行为进行总结分析,并概括出人群异常的三大关键特征. 并在次基础上,分别针对人群特征提取、异常识别技术、异常分类技术以及人群异常识别数据库方面,对人群异常识别技术现状进行总结概括,并并对存在的问题,以及未来发展方向提出了研究的建议和意见. 文章对相关领域的研究具有一定的参考价值.     

虚拟人群仿真技术的研究

在参阅和分析了大量有关文献的基础上，对现有的与实现虚拟人群仿真相关的各种技术、方法进行了总结，特别对虚拟人群仿真技术中比较关键的虚拟人的运动控制方法以及虚拟人群实时可视化两个方面进行了详细的介绍。并应用相关技术建立了艺术体操团体项目编排原型系统，为深入研究虚拟人群的仿真提供有益的参考。     

Intelligent Collectives: Theory,Applications, and Research Challenges

ABSTRACT

Nowadays, along with the rapid development of information technologies, very often solving a common problem is entrusted to many autonomous units (people, systems). With such an approach, one can tap into the so-called collective intelligence (CI)—emerging from the collaboration and competition of many individuals. In this paper, we present recent research on CI related to the effectiveness of using the wisdom of crowds to perform a wide range of problems. For this aim, we first introduce a general framework of CI involving key characteristics of intelligent collectives. Next, we focus on the problem of how diversity and collective cardinality influence collective performance. Then, its applications, which are widely used such as prediction markets and Delphi method, will be presented. Furthermore, some research challenges on the capacity of combining CI with other research fields such as machine learning and social networks are also discussed.     

团体操虚拟编排和演练原型系统

采用有指导的虚拟人群仿真技术，对团体操编排和演练进行计算机仿真，设计并实现了一个团体操编排和演练原型系统，该系统由团体操队形及图案设计子系统和团体操虚拟排练子系统构成．给出了系统结构与功能，并介绍了系统实现的路径规划、避碰和实时绘制等关键技术．实验结果表明，该系统能够为团体操创编人员改进队形与图案变化的设计质量和提高设计效率提供方便，为团体操编排和演练人员提供辅助工具．     

采用截面流量统计的高密度人群行人计数

利用摄像头实现行人计数在智能视频监控领域有着重要的价值,但是行人互相遮挡、噪声、摄像机透视效果和图像背景等问题影响了人群计数的准确性.针对高密度人群场景的行人计数准确率的问题,提出了基于截面流量统计的行人计数方法,该方法基于梯度运动历史图像检测前景,并用有效运动图像改进了基于特征提取的行人计数方法,结合运动速度提取方法实现了行人计数.实验结果表明,提出的计数方法在高密度人群场景中具有较高的准确率和实时性,是一种针对高密度人群有效的行人计数方法.     

一种基于采样点的大规模群体实时三维可视化方法

大规模群体的三维可视化是虚拟现实领域的研究热点之一.目前,由于驱动方法与渲染效率的问题,在虚拟空间中创建的大规模虚拟群体很难同时满足驱动与渲染过程的逼真与实时.该问题对于群体规模庞大,个体外形、动作具有一定程度个性化,需要进行独立运动控制的场景尤为明显.针对这一问题,提出了一个高效的大规模虚拟群体三维可视化方法:首先,通过使用模板派生技术从少量模板模型派生出大量外观各异的个体模型;其次,使用运动数据个性化变形技术实现了运动数据的重用;最后,使用基于采样点的渲染技术实现了大规模数据的实时显示.在上述工作基础上,研究并开发了一套大规模虚拟群体的三维可视化系统,能够方便的在普通PC机上实时、逼真的展示大规模的运动虚拟群体,并实现了60000群众紧急疏散过程的实时三维可视化.     

Classification-oriented dawid skene model for transferring intelligence from crowds to machines

When a crowdsourcing approach is used to assist the classification of a set of items, the main objective is to classify this set of items by aggregating the worker-provided labels. A secondary objective is to assess the workers’ skill levels in this process. A classical model that achieves both objectives is the famous Dawid-Skene model. In this paper, we consider a third objective in this context, namely, to learn a classifier that is capable of labelling future items without further assistance of crowd workers. By extending the Dawid-Skene model to include the item features into consideration, we develop a Classification-Oriented Dawid Skene (CODS) model, which achieves the three objectives simultaneously. The effectiveness of CODS on this three dimensions of the problem space is demonstrated experimentally.     

Vehicle–pedestrian interaction for mixed traffic simulation

Simulation of real‐world traffic scenarios is widely needed in virtual environments. Different from many previous works on simulating vehicles or pedestrians separately, our approach aims to capture the realistic process of vehicle–pedestrian interaction for mixed traffic simulation. We model a decision‐making process for their interaction based on a gap acceptance judging criterion and then design a novel environmental feedback mechanism for both vehicles' and pedestrians' behavior‐control models to drive their motions. We demonstrate that our proposed method can soundly model vehicle–pedestrian interaction behaviors in a realistic and efficient manner and is convenient to be plugged into various traffic simulation systems. Copyright © 2015 John Wiley & Sons, Ltd.     

基于场景结合的大规模动态群体可见性计算方法

动态场景的可见性计算对于大规模场景的实时渲染具有重要意义,其中运动中的大规模群体更给可见性计算带来了很大的开销.针对大规模动态群体在建筑物场景内部运动的情况,提出一种与场景结合的动态群体可见性计算方法.在预处理时,根据个体在不同仿真时刻的位置,将其绑定到相应的场景节点中;在实时绘制时,结合场景的可见性判断结果对动态群体中的个体进行可见性判断.实验结果表明,该方法能高效地剔除动态群体中的不可见个体,使大规模动态场景的实时绘制效率得到明显提高.     

A Novel Cultural Crowd Model Toward Cognitive Artificial Intelligence

Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements; time, capacity, speed, and culture. Cross-cultural variations are increasing the complexity level because each mass and event have different characteristics and challenges. However, no prior study has employed the six Hofstede Cultural Dimensions (HCD) for predicting crowd behaviors. This study aims to develop the Cultural Crowd-Artificial Neural Network (CC-ANN) learning model that considers crowd’s HCD to predict their physical (distance and speed) and social (collectivity and cohesion) characteristics. The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated regulation plan’s time and capacity. We designed the experiments as four groups to analyze the proposed model’s outcomes and extract the interrelations between the HCD of crowd’s grouped individuals and their physical and social characteristics. Furthermore, the extracted interrelations were verified with the dataset’s statistical correlation analysis with a P-value < 0.05. Results demonstrate that the predicted crowd’s characteristics were positively/negatively affected by their considered cultural features. Similarly, analyzing outcomes identified the most influential HCD for predicting crowd behavior. The results also show that the CC-ANN model improves the prediction and learning performance for the crowd behavior because the achieved accepted level of accuracy does not exceed 10 to 20 epochs in most cases. Moreover, the performance improved by 90%, 93% respectively in some cases. Finally, all prediction best cases were related to one or more cultural features with a low error of 0.048, 0.117, 0.010, and 0.014 mean squared error, indicating a novel cultural learning model.