针对复杂跟踪条件下目标的稳健跟踪和精确尺度估计问题,提出了一种基于多层卷积特征融合的目标尺度自适应稳健跟踪算法。算法首先利用VGG-Net-19深层卷积网络架构提取目标候选区域的多层卷积特征,通过相关滤波算法构建二维定位滤波器,得到多层卷积特征并进行加权融合,从而确定目标的中心位置;然后通过对目标区域进行多尺度采样,提取其梯度方向直方图特征构建一维尺度相关滤波器,确定目标的最佳尺度。实验结果表明,与6种当前主流跟踪算法相比,该算法取得了最好的跟踪成功率与精度,同时在跟踪过程中较好地实现了对目标快速尺度变化的自适应跟踪,且具有较快的跟踪速率。

For the problems about robust tracking and precision scale estimation of the visual objects in the complex tracking conditions, a target scale adaptive robust tracking algorithm based on the fusion of multilayer convolutional features is proposed. First, the multilayer convolutional features are extracted from the target candidate area using VGG-Net-19 deep convolutional network architecture. By constructing the two-dimensional location filters by correlation filtering algorithm and fusing the multilayer convolutional features, the center location of the target is determined. Then, through the multi-scale sampling of target, the histogram of oriented gradient features are extracted to construct the one-dimensional scale filter to achieve the optimal scale estimation. The experimental results show that the proposed algorithm gains the best success rate and precision compared with the six state-of-the-art methods. Meanwhile, this algorithm achieves an adaptive tracking to the fast scale changing of target effectively, and possesses a fast tracking speed.