Multi-Camera Panorama Generation with Uncalibrated Camera

在这个项目中，我们处理这样一个场景：有一辆车，车上安装了摄像头，在车辆行驶时记录全景视频/图像；也就是说，来自相机的图像被拼接成一个广角（可能是360度）全景。应用包括民用地形，例如b谷歌地图车，或**军事侦察。在这样的系统中，当一个摄像机发生故障时，必须用一个备用摄像机来替换它。问题是备用相机的校准过程很慢，必须由专业操作员在受控条件下完成。这要求车辆停止操作并返回基地，降低了操作员的能力，直到车辆返回值班。我们建议通过建立一个系统来解决这个问题，通过这个系统，车辆的**司机可以**快速地用备用摄像头替换故障的摄像头，我们的系统将**自动地从未校准的备用摄像头创建全景，足够精确，以便**继续操作。我们建议使用机器学习来实现这一目标。当车辆正常返回基地时，一个新的**摄像头将被安装并由专家进行校准

In this project, we deal with a scenario where there is a vehicle in which cameras are installed to record**panoramic video/images as the vehicle moves; i.e., images from the cameras are stitched together into a**wide-view (possibly 360 degree) panorama. Applications include civilian topography, e.g. Google Map cars, or**military reconnaissance. In such systems, when a camera fails, it must be replaced by a spare camera. The**problem is that the calibration process for the spare camera is slow and must be done by an expert operator in**controlled condition. This requires the vehicle to stop operations and return to base, reducing the capabilities of**the operator until the vehicle returns to duty. We propose to solve this problem by having a system whereby the**driver of the vehicle can quickly replace the failed camera by a spare camera, and our system would**automatically create the panorama from the uncalibrated spare camera, accurately enough for the operation to**continue. We propose to achieve this using machine learning. When the vehicle returns to base normally, a new**camera is then installed and calibrated by an expert.**