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Depth Embedded Recurrent Predictive Parsing Network for Video Scenes

Zhou, Lingli; Zhang, Haofeng; Long, Yang; Shao, Ling; Yang, Jingyu

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Authors

Lingli Zhou

Haofeng Zhang

Ling Shao

Jingyu Yang



Abstract

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.

Citation

Zhou, L., Zhang, H., Long, Y., Shao, L., & Yang, J. (2019). Depth Embedded Recurrent Predictive Parsing Network for Video Scenes. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4643-4654. https://doi.org/10.1109/tits.2019.2909053

Journal Article Type Article
Acceptance Date Apr 1, 2019
Online Publication Date Apr 15, 2019
Publication Date Dec 31, 2019
Deposit Date Sep 1, 2019
Publicly Available Date Mar 29, 2024
Journal IEEE Transactions on Intelligent Transportation Systems
Print ISSN 1524-9050
Electronic ISSN 1558-0016
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 20
Issue 12
Pages 4643-4654
DOI https://doi.org/10.1109/tits.2019.2909053

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