摘要：【中文】本发明公开了耦合虚拟和物理空间人群情感传染控制模拟方法及系统，包括：基于个性化情绪感染规则，制定包含物理空间和虚拟空间的控制方案；将所述控制方案和个性化情绪感染机制相结合，构建个性化情绪感染计算模型；建立性化BA无标度网络，计算个性化情绪感染计算模型在情绪感染发生相变现象时的阈值，获得个体从易感状态到感染状态的数量随相变值的变化；验证所述模型的稳定性和所述模型在情绪感染发生相变现象时的阈值。本发明综合考虑了虚拟和物理空间中个体之间复杂的交互作用，建立了一种新的个性化情绪感染计算模型去研究耦合物理和虚拟空间的个性化情绪感染控制策略。 【EN】The invention discloses a method and a system for simulating the emotional infection control of people in coupling virtual and physical spaces, wherein the method comprises the following steps: formulating a control scheme comprising a physical space and a virtual space based on the personalized emotional infection rule; combining the control scheme with a personalized emotion infection mechanism to construct a personalized emotion infection calculation model; establishing a personalized BA scale-free network, calculating a threshold value of a personalized emotional infection calculation model when the emotional infection has a phase change phenomenon, and obtaining the change of the number of individuals from a susceptible state to an infected state along with the phase change value; and verifying the stability of the model and the threshold value of the model when the emotional infection generates phase transition phenomenon. The invention comprehensively considers the complex interaction between individuals in virtual and physical spaces, and establishes a new individualized emotional infection calculation model to research the individualized emotional infection control strategy coupling the physical space and the virtual space.

摘要：【中文】本公开公开了基于残差网络的人群运动疏散仿真方法及系统，获取待进行人群疏散仿真的视频；从待进行人群疏散仿真的视频中，提取实际的群体内聚性特征和实际的群体集群性特征；基于实际的群体内聚性特征，将待进行人群疏散仿真的视频中的人群划分为若干个实际群组；针对每个实际群组，将当前实际群组中每个实际个体的运动特征和实际的群体集群性特征作为训练好的残差网络的输入值，训练好的残差网络输出当前实际个体下一时间步的预测速度；根据当前实际个体下一时间步的预测速度，生成人群疏散的仿真动画，对人群运动疏散速度进行引导。实验结果表明，该方法能够真实地模拟人群运动过程，并且训练的人群仿真框架可以适用于不同的场景。 【EN】The utility model discloses a residual error network-based crowd movement evacuation simulation method and a system, which are used for obtaining a video to be subjected to crowd evacuation simulation; extracting actual group cohesiveness characteristics and actual group clustering characteristics from a video to be subjected to crowd evacuation simulation; dividing the crowd in the video to be subjected to crowd evacuation simulation into a plurality of actual groups based on the actual crowd cohesion characteristics; aiming at each actual group, taking the motion characteristic and the actual group clustering characteristic of each actual individual in the current actual group as input values of a trained residual error network, and outputting the prediction speed of the current actual individual at the next time step by the trained residual error network; and generating a crowd evacuation simulation animation according to the predicted speed of the next time step of the current actual individual, and guiding the crowd movement evacuation speed. Experimental results show that the method can truly simulate the crowd movement process, and the trained crowd simulation framework can be suitable for different scenes.

摘要：【中文】本公开公开了基于知识和情绪双重驱动的人群疏散仿真方法及系统，包括：获取当前个体原有的知识量；根据当前个体原有的知识量和当前个体从邻居个体获取的知识量，计算出当前个体现有的知识量；根据当前个体原有情绪值和当前个体受周围邻居个体影响的情绪值，计算当前个体现有情绪值；根据当前个体的运动速度、当前个体的初始速度、当前个体的现有知识量、当前个体的邻居个体的初始速度、当前个体的邻居个体的现有知识量，计算当前个体的无碰撞速度；根据当前个体的无碰撞速度，进行人群疏散模拟仿真运动。更加直观地展现疏散效果。 【EN】The utility model discloses a crowd evacuation simulation method and system based on knowledge and emotion dual drive, which comprises the following steps: acquiring the original knowledge quantity of the current individual; calculating the existing knowledge quantity of the current individual according to the original knowledge quantity of the current individual and the knowledge quantity of the current individual acquired from the neighbor individual; calculating the current emotion value of the current individual according to the original emotion value of the current individual and the emotion value of the current individual influenced by surrounding neighbor individuals; calculating the collision-free speed of the current individual according to the movement speed of the current individual, the initial speed of the current individual, the prior knowledge amount of the current individual, the initial speed of the neighbor individual of the current individual and the prior knowledge amount of the neighbor individual of the current individual; and carrying out crowd evacuation simulation motion according to the collision-free speed of the current individual. The evacuation effect is more intuitively displayed.

摘要：【中文】本公开公开了一种基于切换卷积网络的人群计数方法及系统，包括对目标图像进行分块，将图像块输入切换卷积神经网络，对其经过分类器根据密度进行分类；将分类后的图像块通过回归器进行密度图的特征提取，对得到的密度图特征，通过特征拼接，得到结合全局密度图特征的特征图；对结合全局密度特征的特征图经过均值池化和反卷积层的处理，得到目标估计密度图，通过积分，得到目标图像中的人数。切换卷积网络由几个卷积核大小不同的CNN作为密度图预测的回归器，由经训练的选择分类器来对于每一张输入图像选取最优的CNN回归器，将其结果作为最终结果，提高了预测人群数量的准确性和鲁棒性。 【EN】The utility model discloses a crowd counting method and system based on a switching convolution network, which comprises the steps of partitioning a target image, inputting the image block into the switching convolution neural network, and classifying the image block according to density through a classifier; performing feature extraction on the classified image blocks through a regressor, and performing feature splicing on the obtained density map features to obtain a feature map combined with global density map features; and processing the feature map combined with the global density features through a mean pooling layer and a deconvolution layer to obtain a target estimated density map, and obtaining the number of people in the target image through integration. The switching convolution network uses CNNs with different convolution kernel sizes as regressors of density map prediction, selects the optimal CNN regressor for each input image by the trained selection classifier, and uses the result as a final result, so that the accuracy and robustness of the predicted crowd number are improved.

摘要：【中文】本公开公开了一种基于密度信息的人群计数方法、装置、设备以及介质，包括将带有人头位置标注的图像数据集经过预处理得到训练样本集，将待测试图像分别输入至基于目标检测的卷积神经网络和基于密度回归的卷积神经网络中，得到基于目标检测的密度图和基于密度回归的密度图；将基于目标检测的密度图和基于密度回归的密度图进行密度图融合，得到目标估计密度图；对目标估计密度图进行积分计算得到待测试图像中的人数。通过卷积神经网络，融合了基于目标检测人群计数方法的密度图和基于密度回归的密度图，有效的互补了基于检测造成的拥挤地区则会降低其可靠性和基于回归的方法在不知道每个人的位置的情况下倾向于过高地估计低密度的计数的现象。 【EN】The utility model discloses a crowd counting method, a device, equipment and a medium based on density information, which comprises that an image data set with head position marks is preprocessed to obtain a training sample set, and an image to be tested is respectively input into a convolutional neural network based on target detection and a convolutional neural network based on density regression to obtain a density graph based on target detection and a density graph based on density regression; performing density map fusion on the density map based on target detection and the density map based on density regression to obtain a target estimated density map; and performing integral calculation on the target estimated density map to obtain the number of people in the image to be tested. Through the convolutional neural network, a density map based on a target detection crowd counting method and a density map based on density regression are fused, and the phenomenon that the reliability of a crowded area caused by detection is reduced and the low-density counting tends to be excessively estimated by the regression-based method under the condition that the position of each person is unknown is effectively complemented.