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时空相关多通道聚类的运动目标检测 预览

Moving target detection algorithm based on spatiotemporal correlation multi-channel clustering
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摘要 针对某些光照变化、噪声不稳定等多模态场景不适合离线训练背景模型来提取目标信息的问题,在基于混合高斯的背景建模的基础上,利用帧间差分与邻域相似性实现模型初始参数的选取;提出将随机子采样与邻域空间传播理论相结合改进参数更新过程;在时间维度上建立观测向量,实现模型参数的优化,加快模型收敛速度;并将颜色信息和梯度相融合实现基于多特征的多通道背景模型的建立,采用背景点的随机采样策略简化多通道模型建立的计算量,最终实现复杂环境下的运动目标的检测.实验表明,算法在抑制鬼影、动态背景和遮挡等方面有良好的检测性能,且执行效率能够满足实时计算的需求. In the process of tracking target, certain multi-modal background scenes are not suitable for the off-line training model, and moving target detection is affected as background in the current video environment is mostly multi-modal scene with much noise, and the characters of moving targets irregularly change,which,therefore, requires a more stable and robust moving target detection algorithm. To solve this problem,taking advantage of spatiotemporal relationship learning, the mixed Gaussian model(GMM) is improved in three aspects.First, the initialization method combining five-frame difference and intra-frame neighborhood average is proposed to obtain the initial parameters of the mixed Gaussian model. The five-frame difference method is introduced to obtain the initial parameters of the model, so that the background model is closer to the real scene. The intra-frame neighborhood average value is introduced, and an accumulation matrix CA is proposed to record the number of neighboring pixel points, then to enhance the information relevant to the neighborhood.This process can reduce the discontinuity of the target.Second, the calculation method of the neighborhood correlation is introduced to update the parameter of Gaussian model. Since the single pixel feature is related to the neighborhood random correlation, the random subsampling technology and neighborhood spatial propagation theory are combined together, and the execution efficiency is taken into account to simplify the process of updating model. To speed up the model convergence,an observation vector is built in the time dimension to optimize the model parameters, and the weight w is gained based on the posterior probability.Then, the color-gradient method incooperated with the color HSI space and gradient information is adopted in this paper to complete the multi-channel Gaussian mixture model. The initial and the updated parameters of the Gaussian model in each channel can be acquired via the above steps. To simplify the computation of three channels, th
作者 徐艳 王培光 杨青 董江涛 Xu Yan;Wang Pei-Guang;Yang Qing;Dong Jiang-Tao(College of Electronic Information Engineering, Hebei University, Baoding 071002, China;Department of Electronic and Optical Engineering, Army Engineering University Shijiazhuang Campus, Shijiazhuang 050000, China;College of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, China;The 54th Research Institute of China Electronics Science and Technology Corporation, Shijiazhuang 050000, China)
出处 《物理学报》 SCIE EI CAS CSCD 北大核心 2019年第16期202-211,共10页 Acta Physica Sinica
基金 国家自然科学基金(批准号:11771115)资助的课题.
关键词 混合高斯 随机子采样 邻域相关 多通道 Gaussian mixture random subsampling neighborhood correlation multi-channel
作者简介 通信作者:王培光,E-mail:pgwang@hbu.edu.cn.
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