基于可变形卷积的单帧图像眼球定位追踪

针对目前眼球定位追踪算法存在的眼球定位精准度不高问题，以及为了改进眼球追踪算法的精准度并保证一定的图片处理速度，将可变形卷积网络应用于YOLO网络，对特征分布提取层面进行改进。利用可变形卷积的形变建模能力对卷积核中的各个采样点的位置增加一定的偏移变量，从而从原始单帧图像中提取更具有表征特征的信息，并与先进眼球定位追踪检测网络进行了实验对比。研究表明，可变形卷积YOLO网络的精准度可以达到0.685，平均处理图片刷新率达42帧/s，优于原YOLO网络以及其他眼球定位追踪检测网络。

Single-frame image eyeball tracking based on deformable convolution

In order to improve the accuracy of the eye tracking algorithm and ensure a certain image processing speed, this paper proposes to combine the deformable convolution method to improve the feature distribution extraction level. The fixed-size sampling in the standard convolution makes it difficult for the learning network to adapt to the geometric deformation of the image. In order to solve this limitation, the deformation modeling ability of deformable convolution is used to add a certain offset variable to the position of each sampling point in the convolution kernel. So as to achieve the extraction of potential features, the single frame of the original image is described. According to the current research, the deformable convolution has made preliminary applications in the field of computer vision. After comparing with the advanced eyeball positioning tracking detection network experiment, the accuracy of the deformable convolutional YOLO network can reach 0.685, and the average image processing speed can reach 42 frames per second, which is better than the original YOLO network and the advanced eyeball and location tracking detection network.