Andra Petrovai, Arthur D. Costea and Sergiu Nedevschi
Proceedings of 2017 IEEE Intelligent Vehicles Symposium (IV 17), Los Angeles, CA, USA, 11-14 June 2017, pp. 448-455
Scene labeling enables very sophisticated and powerful applications for autonomous driving. Training classiﬁers for this task would not be possible without the existence of large datasets of pixelwise labeled images. Manually annotating a large number of images is an expensive and time consuming process. In this paper, we propose a new semi-automatic annotation tool for scene labeling tailored for autonomous driving. This tool signiﬁcantly reduces the effort of the annotator and also the time spent to annotate the data, while at the same time it offers the necessary features to produce precise pixel-level semantic labeling. The main contribution of our work represents the development of a complex annotation framework able to generate automatic annotations for 20 classes, which the user can control and modify accordingly. Automatic annotations are obtained in two separate ways. First, we employ a pixelwise fully-connected Conditional Random Field (CRF). Second, we perform grouping of similar neighboring superpixels based on 2D appearance and 3D information using a boosted classiﬁer. Polygons represent the manual correction mechanism for the automatic annotations.