University of Maryland, College Park, MD 20742, USA.
IEEE Trans Image Process. 2010 Aug;19(8):2221-32. doi: 10.1109/TIP.2010.2046368. Epub 2010 Mar 22.
This paper presents an approach for video metrology. From videos acquired by an uncalibrated stationary camera, we first recover the vanishing line and the vertical point of the scene based upon tracking moving objects that primarily lie on a ground plane. Using geometric properties of moving objects, a probabilistic model is constructed for simultaneously grouping trajectories and estimating vanishing points. Then we apply a single view mensuration algorithm to each of the frames to obtain height measurements. We finally fuse the multiframe measurements using the least median of squares (LMedS) as a robust cost function and the Robbins-Monro stochastic approximation (RMSA) technique. This method enables less human supervision, more flexibility and improved robustness. From the uncertainty analysis, we conclude that the method with auto-calibration is robust in practice. Results are shown based upon realistic tracking data from a variety of scenes.
本文提出了一种视频计量方法。通过对未经校准的固定摄像机获取的视频，我们首先基于跟踪主要位于地面上的移动对象来恢复场景的灭点和垂直点。利用移动对象的几何特性，构建了一个用于同时对轨迹进行分组和估计灭点的概率模型。然后，我们对每一帧应用单视图测量算法以获得高度测量值。最后，我们使用最小中位数平方（LMedS）作为稳健代价函数和 Robbins-Monro 随机逼近（RMSA）技术来融合多帧测量值。这种方法需要较少的人工监督，具有更高的灵活性和改进的鲁棒性。通过不确定性分析，我们得出结论，自动校准的方法在实际中具有稳健性。结果基于来自各种场景的真实跟踪数据显示。
