Gaze tracking is an important technology in many domains. Techniques such as Convolutional Neural Networks (CNN) has allowed the invention of gaze tracking method that relies only on commodity hardware such as the camera on a personal computer. It has been shown that the full-face region for gaze estimation can provide better performance than from an eye image alone. However, a problem with using the full-face image is the heavy computation due to the larger image size. This study tackles this problem through compression of the input full-face image by removing redundant information using a novel learnable pooling module. The module can be trained end-to-end by backpropagation to learn the size of the grid in the pooling filter. The learnable pooling module keeps the resolution of valuable regions high and vice versa. This proposed method preserved the gaze estimation accuracy at a certain level when the image was reduced to a smaller size.
翻译:银色跟踪是许多领域的重要技术。 革命神经网络(CNN)等技术允许发明只依靠个人计算机上相机等商品硬件的凝视跟踪方法。 已经显示,全面视觉估计区域比光眼图像的显示效果更好。 但是,使用全面图像的问题是由于图像大小较大而导致的重度计算。 本研究通过使用新颖的可学习集成模块删除多余的信息,压缩输入全面图像来解决这一问题。 该模块可以通过反向调整来接受培训,学习集合过滤器中的网格大小。 可学习的集合模块使得有价值的区域的分辨率高,反之亦然。 在图像缩小到较小尺寸时,这一拟议方法将视觉估计的精确度保持在一定水平上。