稀疏表示下大场景视频图像运动目标跟踪仿真  

Sparse representation of large scene video image motion object tracking simulation

作　　者：刘洪鹏 孙永佼 LIU Hong-peng;SUN Yong-jiao(College of Computer Science and Engineering,Ningxia Institute of Science and Technology,Ningxia Shizuishan 753000,China;College of Computer Science and Engineering,Northeastern University,Liaoning Shenyang 110819,China)

机构地区：[1]宁夏理工学院计算机科学与工程学院,宁夏石嘴山753000 [2]东北大学计算机科学与工程学院,辽宁沈阳110819

出　　处：《计算机仿真》2024年第5期333-337,共5页Computer Simulation

摘　　要：大场景视频中存在复杂的背景信息,这些背景信息与目标的外观相似,导致难以准确跟踪目标。为了解决这一问题,提出基于稀疏表示的大场景视频图像运动目标跟踪方法。通过提取视频图像haar-like特征并对特征投影压缩,在字典中融入背景信息,构造超完备字典。改进传统粒子滤波,提出基于残差的Unscented粒子滤波算法,在传统稀疏表示中添加判别函数生成判别稀疏表示,利用L1范数最小化求解候选目标稀疏系数,在字典不断更新下通过基于残差的Unscented粒子滤波算法,实现大场景视频图像运动目标跟踪。实验结果表明,所提方法目标跟踪精度和重合度均在0.9以上,且跟踪平均时间短。In large scene videos,there is a large amount of complex background information,which is similar to the appearance of the target,so it is difficult to accurately track the target.To solve this problem,this article put forward a method for tracking motion targets in large scene video images based on sparse representation.Firstly,Haarlike features were extracted from video images and compressed.Secondly,background information was incorporated into the dictionary to construct an over-complete dictionary.Then,the traditional particle filter was improved.Meanwhile,an Unscented particle filter algorithm was proposed based on residual error.Moreover,a discriminant function was added to the traditional sparse representation,so that a discriminant sparse representation was generated.Based on L1 norm minimization,the sparse coefficients of the candidate target were solved.Under continuous dictionary updates,the motion target tracking was realized through the residual-based Unscented particle filter algorithm.Experimental results show that the target tracking accuracy and coincidence rate are more than 0.9,and the average tracking time was short.

关 键 词：稀疏表示 大场景视频图像 运动目标跟踪 HAAR-LIKE特征 粒子滤波 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]