运动状态与尺度估计的核相关目标跟踪方法

视觉跟踪在视频智能监控和机器人等领域有着广泛应用。基于相关滤波分类器,提出了具有运动状态估计和目标尺度估计的视觉目标跟踪方法。该方法将粒子滤波与核相关滤波方法相结合,首先估算运动目标的位置,然后执行尺度相关滤波器来估算目标的尺度,以使算法对尺度变化的运动目标具有更强的适应能力。该方法在传统的KCF跟踪算法的基础上引入了一种基于概率的运动状态估计方法,可以获得更加稳定的目标信息,并减少背景干扰信息的引入,从而在复杂场景下具有更强的抗干扰性。使用benchmark数据集对所提方法进行了测试实验,并和其他已有的若干视觉跟踪方法进行了对比实验,结果验证了所提算法的高效性,且所提方法在目标尺度变化、光照变化、姿态变化、部分遮挡、旋转及快速运动等复杂情况下均有较强的适应性。

Visual Object Tracking Method with Motion Estimation and Scale Estimation

Visual tracking has a wide range of applications in various fields such as video intelligent monitoring and eyes of robot.Based on discriminatory correlation filter,a visual target tracking method with motion estimation and scale estimation method was proposed.First,this method combines translation kernel correlation filter and particle filter method to estimate the position of moving targets on the frame.And then,it executes the scale correlation filter to be stronger ability to adapt scale change of moving object.In traditional KCF tracking algorithm,this method introduces a motion state estimation method based on probability,which can obtain more stable signal of target,and reduces the introduction ofthe background interference information at the same time,leading to it has stronger anti-jamming in complex scenarios.On benchmark data set,we took experiment to test this method,and compared our method with the current advanced tracking methods to verify the efficiency of the proposed algorithm in this paper.It has strong adaptability under complex conditions,the change of scale,illumination,change of pose,partial sheltering,rotating and rapid movement etc.