论文标题
基于视频的实时位置跟踪器
Video based real-time positional tracker
论文作者
论文摘要
我们建议使用视频作为输入的系统，以实时跟踪对象相对于周围环境的位置。使用的神经网络对来自我们自己的自动化发电机的100％合成数据集进行了培训。位置跟踪器依赖于1到N摄像机的范围，该摄像机围绕着选择的舞台。 该系统通过了解相机形成的重叠矩阵来返回相对于更广阔世界的轨道对象的位置，因此可以将其推断到现实世界的坐标中。 在大多数情况下，我们要获得的更新速率和定位精度比任何现有的基于GPS的系统，尤其是室内对象或透明天空遮挡的系统。
We propose a system that uses video as the input to track the position of objects relative to their surrounding environment in real-time. The neural network employed is trained on a 100% synthetic dataset coming from our own automated generator. The positional tracker relies on a range of 1 to n video cameras placed around an arena of choice. The system returns the positions of the tracked objects relative to the broader world by understanding the overlapping matrices formed by the cameras and therefore these can be extrapolated into real world coordinates. In most cases, we achieve a higher update rate and positioning precision than any of the existing GPS-based systems, in particular for indoor objects or those occluded from clear sky.
