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Depth embedded recurrent predictive parsing network for video scenes.

Zhou, Lingli and Zhang, Haofeng and Long, Yang and Shao, Ling and Yang, Jingyu (2019) 'Depth embedded recurrent predictive parsing network for video scenes.', IEEE transactions on intelligent transportation systems., 20 (12). pp. 4643-4654.

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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/TITS.2019.2909053
Publisher statement:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:01 April 2019
Date deposited:03 September 2019
Date of first online publication:15 April 2019
Date first made open access:03 September 2019

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