基于深度估计和特征融合的尺度自适应目标跟踪算法

Scale adaptive target tracking algorithm based on depth estimation and feature fusion

针对核相关滤波目标跟踪算法(KCF)使用单特征来描述所跟踪的目标,在复杂环境下,目标尺度发生较大变化时,无法准确跟踪目标的问题,提出基于深度估计和特征融合的尺度自适应目标跟踪算法。首先利用深度神经网络估计视频序列中目标的深度,建立并训练深度-尺度估计模型;在跟踪过程中,融合目标方向梯度直方图(HOG)特征和CN(Color Name)特征训练相关滤波器,利用深度估计网络得到目标深度值,并利用深度-尺度估计模型得到目标的尺度值,从而在目标尺度发生变化时,能够调整目标框大小,实现尺度自适应的目标跟踪算法。实验结果表明,与经典的KCF算法相比,可获得更高的精度,与尺度自适应的判别型尺度空间跟踪(DSST)算法相比,在尺度变化较大时,跟踪速度更快;在环境复杂、目标被遮挡时,鲁棒性更好。

The correlation filter target tracking algorithm kernelized correlation filters(KCF)utilizes a single feature to describe the target,which cannot accurately track the target in complex environments,or when the target scale changes significantly.To address these problems,a scale adaptive target tracking algorithm based on depth estimation and feature fusion was proposed.Firstly,the depth of the target in the video was estimated using the depth estimation network,and the depth-scale estimation model was built and trained.In the tracking,the histogram of oriented gradient(HOG)features and color name(CN)features of the target were fused to train the correlation filter,and the depth of the target was obtained using the depth estimation network.In addition,the depth-scale estimation model was employed to obtain the scale of the target.Thus,the target size can be adjusted when the target scale changes,and the scale adaptive target tracking algorithm is realized.The experimental results show that compared with the classical algorithm KCF,higher accuracy can be obtained,and compared with the scale adaptive algorithm discriminative scale space tracking(DSST),the tracking speed is faster when the scale changes greatly,and the robustness is better in cases of complex environments and obscured targets.