融合HOG特征的相关滤波视频跟踪

计算机视觉领域的目标跟踪已取得巨大进展，但在视频跟踪中,平面外旋转和形状变化的性能方面还有提升空间。本文提出一种基于方向梯度直方图HOG特征，结合图像灰度值把HOG特征加以融合和分解，以提升视频跟踪的变形和尺度变换的性能。首先提取目标区域的HOG的31维特征和灰度值；其次,将灰度值作为1维特征,与HOG特征融合成32维向量HOG32；进而将HOG32分解成2部分特征，分别为HOG1和HOG2；最后,通过对HOG1、HOG2和HOG32特征响应值的比较，选择最大值位置作为预测的下一帧的位置。实验在OTB-2013和OTB-2015这2个数据集上进行,与其他5个算法的比较结果表明，该方法在平面外旋转、变形、复杂背景等方面获得良好效果。

Correlation Filter Video Tracking Based on Fusion of HOG Feature

Although great progress has been made in the field of computer vision target tracking, the performance of out-of-plane rotation and shape change in video tracking need to be improved. Here, HOG feature based on directional gradient histogram is proposed. Combined with the gray value of the image, the HOG feature is fused and decomposed to improve the performance of the deformation and scale transformation of the video tracking. Firstly, the 31-dimensional features of the HOG and the gray value of the image are extracted from the target region. Secondly, the gray value is regarded as one-dimensional feature, then the gray value is fused with HOG feature into 32-dimensional vector HOG32. Then the HOG32 is decomposed into two parts, namely, HOG1 and HOG2. Finally, compared the response values of HOG1,HOG2 and HOG32, the maximum position is selected as the position of the next frame predicted. The experiment is compared with the other five algorithms on OTB-2013 and OTB-2015 datasets. The results demonstrate that our method achieve better results in out-of-plane rotation, deformation and complex background.