基于核相关滤波器和分层卷积特征的长时间目标跟踪

Long-term Object Tracking Based on Kernelized Correlation Filter and Hierarchical Convolution Features

针对长时间目标跟踪中出现的目标形变、尺度变化、目标遮挡以及离开视野等问题,提出一种基于核相关滤波器和分层卷积特征的长时目标跟踪算法。首先,利用预训练的卷积神经网络模型提取分层卷积特征来训练核相关滤波器,进行位置估计。其次,构建目标尺度金字塔,进行尺度估计。最后,为了应对目标遮挡以及离开视野导致跟踪失败的情况,训练一个在线支持向量机进行目标再检测,从而实现长时间目标跟踪。在长时间目标跟踪数据集上的测试结果表明:所提算法的精度分别比其他几种主流跟踪算法HCF,LCT,DSST,KCF和TLD高出7%,15%,17%,21%和50%。

Aiming at the problems such as deformation,scale variation,target occlusion,and out of sight during long-term object tracking,this paper proposed a long-term object tracking algorithm based on kernelized correlation filter and hierarchical convolution feature.Firstly,the pre-trained convolution neural network is applied to extract the hierarchical convolution feature,so as to train correlation filter and estimate location.Then the target scale pyramid is constructed to estimate scale.In order to prevent tracking failure caused by target occlusion and tanget leaving the field of vision,an online support vector machine is trained for target re-detection to achieve long-term tracking.Experimental results on long-term object tracking dataset show that the accuracy of the proposed algorithm is 7%,15%,17%,21% and 50% higher than that of HCF,LCT,DSST,KCF and TLD.