基于人群分布与运动动能的群体异常行为检测
发布时间：2019-05-24 08:35
【摘要】：本文使用粒子熵方法来描述群体的分布信息,该方法首先将粒子均匀地洒在每一帧视频上,用视频帧中粒子的流动来描述视频中群体的运动,粒子速度的计算是根据周围像素点的光流值计算得到的,文中把运动速度大于某个阈值的粒子定义为运动粒子。将运动的粒子分别投影到水平与垂直坐标轴上,计算粒子在水平与垂直方向上的概率分布,通过粒子的概率分布计算得到粒子的熵值,用运动粒子的熵值来描述人群的分布信息。最后,根据粒子的运动速度计算得到粒子的运动动能。通常情况下正常人群行为的数量远远大于异常人群行为的数量,因而群体异常行为检测是一个不平衡问题。根据高斯混合模型(GMM)在处理不平衡问题上的优势,本文利用GMM对正常群体行为进行建模。在建模阶段使用的训练样本中只包含正常的群体行为,利用人群分布的粒子熵值与人群运动的动能来分别建立正常群体行为的高斯混合模型。在异常群体行为检测阶段,提取出待检测视频的人群分布特征值与人群运动动能特征值,使用提取的特征值在建模阶段建立的高斯混合模型上计算其概率,若在两个模型上计算得到的概率值都小于阈值时,则该特征所对应的视频序列中有异常群体行为。本文在包含聚集与分散事件的公共可用数据集UMN数据集与PETS2009数据集上进行了异常群体行为检测的实验,实验验证了本文方法能有效、准确地检测出群体的异常行为。
[Abstract]:In this paper, the particle entropy method is used to describe the distribution information of the group. Firstly, the particles are sprinkled evenly on each frame of video, and the flow of particles in the video frame is used to describe the motion of the group in the video. The calculation of particle velocity is based on the optical flow value of surrounding pixel points. In this paper, the particle whose velocity is greater than a certain threshold is defined as moving particle. The moving particles are projected on the horizontal and vertical coordinate axis respectively, the probability distribution of the particles in the horizontal and vertical directions is calculated, and the entropy value of the particles is obtained by calculating the probability distribution of the particles. The entropy value of moving particles is used to describe the distribution information of the population. Finally, the kinetic energy of the particle is calculated according to the velocity of the particle. In general, the number of normal population behavior is much larger than that of abnormal population behavior, so group abnormal behavior detection is an unbalanced problem. According to the advantages of Gao Si mixed model (GMM) in dealing with unbalanced problems, this paper uses GMM to model the behavior of normal groups. The training samples used in the modeling stage only contain the normal group behavior. The Gao Si mixed model of the normal group behavior is established by using the particle entropy value of the population distribution and the kinetic energy of the population movement respectively. In the stage of abnormal group behavior detection, the eigenvalues of population distribution and kinetic energy of the video to be detected are extracted, and the probability of the extracted eigenvalues is calculated on the Gao Si hybrid model established in the modeling stage. If the probability values calculated on both models are less than the threshold, then the video sequence corresponding to this feature has abnormal group behavior. In this paper, the experiments of abnormal group behavior detection are carried out on the common available dataset UMN dataset and PETS2009 dataset containing aggregation and decentralized events. The experimental results show that the proposed method can detect the abnormal behavior of the group effectively and accurately.
【学位授予单位】：西安理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
本文编号：2484720
[Abstract]:In this paper, the particle entropy method is used to describe the distribution information of the group. Firstly, the particles are sprinkled evenly on each frame of video, and the flow of particles in the video frame is used to describe the motion of the group in the video. The calculation of particle velocity is based on the optical flow value of surrounding pixel points. In this paper, the particle whose velocity is greater than a certain threshold is defined as moving particle. The moving particles are projected on the horizontal and vertical coordinate axis respectively, the probability distribution of the particles in the horizontal and vertical directions is calculated, and the entropy value of the particles is obtained by calculating the probability distribution of the particles. The entropy value of moving particles is used to describe the distribution information of the population. Finally, the kinetic energy of the particle is calculated according to the velocity of the particle. In general, the number of normal population behavior is much larger than that of abnormal population behavior, so group abnormal behavior detection is an unbalanced problem. According to the advantages of Gao Si mixed model (GMM) in dealing with unbalanced problems, this paper uses GMM to model the behavior of normal groups. The training samples used in the modeling stage only contain the normal group behavior. The Gao Si mixed model of the normal group behavior is established by using the particle entropy value of the population distribution and the kinetic energy of the population movement respectively. In the stage of abnormal group behavior detection, the eigenvalues of population distribution and kinetic energy of the video to be detected are extracted, and the probability of the extracted eigenvalues is calculated on the Gao Si hybrid model established in the modeling stage. If the probability values calculated on both models are less than the threshold, then the video sequence corresponding to this feature has abnormal group behavior. In this paper, the experiments of abnormal group behavior detection are carried out on the common available dataset UMN dataset and PETS2009 dataset containing aggregation and decentralized events. The experimental results show that the proposed method can detect the abnormal behavior of the group effectively and accurately.
【学位授予单位】：西安理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
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