Motion Based Masking of a Moving Vehicle's Environment
Tamas Meszegeto, Benedek Tass, Mátyás Szántó · 2019
The problem of autonomous vehicle navigation requires the use of high-definition and well-maintained maps. Such a map can be constructed using the method developed for the so-called CrowdMapping architecture. This paper proposes a method for constructing masks for such map creation purposes via segmenting dynamic and static regions of an image sequence. The segmentation is performed by comparing a calculated and a predicted optical flow field. The proposed segmentation algorithm contains a single image depth estimation part for predicting the expected optical flow field. The comparison method of the two flow fields is also presented in this paper. The proposed method has been evaluated both qualitatively and quantitatively using the KITTI vision dataset, and achieved a filtering error of 7...12% compared to manually prepared ground truth images.