School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.
Sensors (Basel). 2018 Jul 12;18(7):2249. doi: 10.3390/s18072249.
The advance of scene understanding methods based on machine learning relies on the availability of large ground truth datasets, which are essential for their training and evaluation. Construction of such datasets with imagery from real sensor data however typically requires much manual annotation of semantic regions in the data, delivered by substantial human labour. To speed up this process, we propose a framework for semantic annotation of scenes captured by moving camera(s), e.g., mounted on a vehicle or robot. It makes use of an available 3D model of the traversed scene to project segmented 3D objects into each camera frame to obtain an initial annotation of the associated 2D image, which is followed by manual refinement by the user. The refined annotation can be transferred to the next consecutive frame using optical flow estimation. We have evaluated the efficiency of the proposed framework during the production of a labelled outdoor dataset. The analysis of annotation times shows that up to 43% less effort is required on average, and the consistency of the labelling is also improved.
基于机器学习的场景理解方法的进展依赖于大规模的地面实况数据集的可用性，这些数据集对于它们的训练和评估至关重要。然而，使用真实传感器数据的图像构建这样的数据集通常需要对数据中的语义区域进行大量的手动标注，这需要大量的人力劳动。为了加快这个过程，我们提出了一个用于移动摄像机（例如安装在车辆或机器人上的摄像机）拍摄的场景的语义标注的框架。它利用可获得的场景的 3D 模型将分割的 3D 物体投影到每个摄像机帧中，以获得相关 2D 图像的初始标注，然后由用户进行手动细化。细化后的标注可以使用光流估计转移到下一个连续的帧。我们已经在一个标注的户外数据集的制作过程中评估了所提出的框架的效率。标注时间的分析表明，平均需要的工作量减少了 43%，标注的一致性也得到了提高。
