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IEEE Semantic segmentation-based scene parsing plays an important role in automatic driving and autonomous navigation. However, most of the previous models only consider static images, and fail to parse sequential images because they do not take the spatial-temporal continuity between consecutive frames in a video into account. In this paper, we propose a depth embedded recurrent predictive parsing network (RPPNet), which analyzes preceding consecutive stereo pairs for parsing result. In this way, RPPNet effectively learns the dynamic information from historical stereo pairs, so as to correctly predict the representations of the next frame. The other contribution of this paper is to systematically study the video scene parsing (VSP) task, in which we use the RPPNet to facilitate conventional image paring features by adding spatial-temporal information. The experimental results show that our proposed method RPPNet can achieve fine predictive parsing results on cityscapes and the predictive features of RPPNet can significantly improve conventional image parsing networks in VSP task.
Author(s): Zhou L, Zhang H, Long Y, Shao L, Yang J
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Intelligent Transportation Systems
Year: 2019
Volume: 20
Issue: 12
Pages: 4643-4654
Print publication date: 01/12/2019
Online publication date: 15/04/2019
Acceptance date: 31/03/2019
ISSN (print): 1524-9050
ISSN (electronic): 1558-0016
Publisher: IEEE
URL: https://doi.org/10.1109/TITS.2019.2909053
DOI: 10.1109/TITS.2019.2909053
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