This article presents a new method for automatic calibration of surveillance cameras. We are dealing with traffic surveillance, and therefore, the camera is calibrated by observing vehicles; however, other rigid objects can be used instead. The proposed method is using keypoints or landmarks automatically detected on the observed objects by a convolutional neural network. By using fine-grained recognition of the vehicles (calibration objects), and by knowing the 3D positions of the landmarks for the (very limited) set of known objects, the extracted keypoints are used for calibration of the camera, resulting in internal (focal length) and external (rotation, translation) parameters and scene scale of the surveillance camera. We collected a dataset in two parking lots and equipped it with a calibration ground truth by measuring multiple distances in the ground plane. This dataset seems to be more accurate than the existing comparable data (GT calibration error reduced from 4.62 % to 0.99 %). Also, the experiments show that our method overcomes the best existing alternative in terms of accuracy (error reduced from 6.56 % to \(4.03\,\%\)) and our solution is also more flexible in terms of viewpoint change and other.

本文介绍了一种自动校准监视摄像机的新方法。我们正在进行交通监控，因此，通过观察车辆对摄像机进行校准；但是，也可以使用其他刚性物体代替。建议的方法是使用关键点或地标通过卷积神经网络自动在观察对象上检测到。通过使用对车辆（校准对象）的细粒度识别，并通过了解（非常有限的）一组已知对象的地标的3D位置，提取的关键点将用于相机的校准，从而产生内部（焦点）长度）和外部（旋转，平移）参数以及监视摄像机的场景比例。我们在两个停车场中收集了一个数据集，并通过测量地平面中的多个距离为它配备了校准地面真相。该数据集似乎比现有的可比较数据更准确（GT校准误差从4.62％降低到0.99％）。实验还表明，我们的方法在准确性方面克服了现有的最佳方法（误差从6.56％降低到\（4.03 \，\％\）），我们的解决方案在视点更改和其他方面也更加灵活。